{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34097189", "kind": "science-record", "revision": 6, "content_hash": "sha256:29a0cf32d8a3fcd7762467682a079bb7b4dc33b0ef35b2b14a4be992ef5a5d26", "created_at": "2026-11-01T15:41:38Z", "updated_at": "2026-11-01T21:25:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020.", "doi": "10.1007/s10555-021-09969-z", "pmid": "34097189", "authors": ["Simona Giunta"], "journal": "Cancer metastasis reviews", "year": 2021, "abstract": "Cancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer's non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.", "claims": [{"id": "claim-method", "statement": "To accomplish this task, a new software package, DriverPower, combines two state-of-the-art methods, mutational burden and functional impact evidence, to identify a cancer driver [3].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34097189", "locator": "sections[4] \"Main paper: genetic changes in cancer by Pan-Cancer Analysis of Whole Genomes\" paragraph 2", "quote": "To accomplish this task, a new software package, DriverPower, combines two state-of-the-art methods, mutational burden and functional impact evidence, to identify a cancer driver [3]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The PCAWG collected WGS data with a mean read coverage of 39× from 2605 primary tumors and 173 metastatic lesions, as well as healthy tissue control. All the sequences obtained within the framework of the PCAWG Project belong to 38 different histological cancer subtypes. RNA-sequencing data were available for 1222 donors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34097189", "locator": "sections[2] \"PCAWG cohort description and methodologies\" paragraph 2", "quote": "The PCAWG collected WGS data with a mean read coverage of 39× from 2605 primary tumors and 173 metastatic lesions, as well as healthy tissue control. All the sequences obtained within the framework of the PCAWG Project belong to 38 different histological cancer subtypes. RNA-sequencing data were available for 1222 donors."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:34097189"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34097189", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s10555-021-09969-z", "sha256": "sha256:928c9b1abf663ad6a4d7203f989856fc8af08e5a63faf81ce877b92824413b9c", "bytes": 76449, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s10555-021-09969-z", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8180541", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "asset:table:pmid:34097189:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:01805c9a35581f5ea2d7774b32743044c086a52944446237b62d21a6c23da1b5", "bytes": 4960, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[1]/table-wrap[1]#Tab1", "version": null, "note": "PCAWG Project major findings reviewed here. The published collection of papers can be accessed on the Nature website landing page for the PCAWG Consortium (www.nature.com/collections/afdejfafdb).", "structure": {"pmcid": "PMC8180541", "section_index": 1, "section_title": "Introduction", "row_count": 15, "column_count": 4}, "table": {"label": "Table 1", "caption": "PCAWG Project major findings reviewed here. The published collection of papers can be accessed on the Nature website landing page for the PCAWG Consortium (www.nature.com/collections/afdejfafdb).", "rows": [["Publication", "Brief description", "Chapter", "Reference"], ["Campbell PJ, Getz G, Korbel JO, Stuart JM, Jennings JL, Stein LD, et al. Pan-cancer analysis of whole genomes. Nature. 202010.1038/s41586-020-1969-6", "Identified driver mutations across cancer genomes", "3.1", "[2]Preprint [75]"], ["Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, et al. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature. 202010.1038/s41586-020-1965-x", "Analysis of the 13% of tumor samples that have non-coding mutations that drive cancer", "3.2", "[10]Preprint [76]"], ["Alexandrov LB, Kim J, Haradhvala NJ, Huang MN, Tian Ng AW, Wu Y, et al. The repertoire of mutational signatures in human cancer. Nature. 202010.1038/s41586-020-1943-3", "Identified new signatures of mutational processes that cause base substitutions, small insertions and deletions, and structural variation in cancer", "3.3", "[15]Preprint [77]"], ["Li Y, Roberts ND, Wala JA, Shapira O, Schumacher SE, Kumar K, et al. Patterns of somatic structural variation in human cancer genomes. Nature. 202010.1038/s41586-019-1913-9", "Identified new signatures of mutational processes that cause larger-scale structural variations associated with cancer", "3.3", "[16]Preprint [78]"], ["Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, et al. The evolutionary history of 2,658 cancers. Nature. 2020 10.1038/s41586-019-1907-7", "Analysis of the timings and mutational patterns in the evolution of tumors to map the progression and occurrence of each driver", "3.4", "[29]Preprint [79]"], ["Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, et al. Genomic basis for RNA alterations in cancer. Nature. 202010.1038/s41586-020-1970-0", "Describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes, and promoter activity", "3.5.1", "[22]Preprint [80]"], ["Zhang Y, Chen F, Fonseca NA, He Y, Fujita M, Nakagawa H, et al. High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations. Nature Communications. 202010.1038/s41467-019-13885-w", "Analysis of the diverse transcriptional consequences of gene deregulation by non-coding regions in cancer", "3.5.2", "[33]Preprint [81]"], ["Rodriguez-Martin B, Alvarez EG, Baez-Ortega A, Zamora J, Supek F, Demeulemeester J, et al. Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition. Nature Genetics. 2020https://doi. org/10.1038/s41588-019-0562-0", "Evaluates “jumping” of retrotransposable elements as a driver of cancer-associated mutagenesis", "3.6", "[24]Preprint [82]"], ["Sieverling L, Hong C, Koser SD, Ginsbach P, Kleinheinz K, Hutter B, et al. Genomic footprints of activated telomere maintenance mechanisms in cancer. Nature communications10.1038/s41467-019-13824-9", "Explores different known and still unknown pathways used by cancer to maintain their telomeres", "3.7", "[5]Preprint [83]"], ["Yuan Y, Ju YS, Kim Y, Li J, Wang Y, Yoon CJ, et al. Comprehensive molecular characterization of mitochondrial genomes in human cancers. Nature Genetics. 202010.1038/s41588-019-0557-x", "Mutational analysis of mitochondrial DNA in cancer", "3.8", "[49]Preprint [84]"], ["Akdemir KC, Le VT, Sahaana C, Li Y, Group P-SVW, Verhaak RG, et al. Chromatin Folding Domains Disruptions by Somatic Genomic Rearrangements in Human Cancers. Nat Genet. 201910.1038/s41588-019- 0564-y", "Alterations of 3D genome architecture in cancer", "3.9", "[23]Preprint [85]"], ["Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, et al. Pathway and network analysis of more than 2500 whole cancer genomes. Nature Communications. 202010.1038/s41467-020-14351-8", "Establishes the most commonly mutated pathways and molecular processes in driving cancer formation and progression", "3.10", "[52]Preprint [86]"], ["Cortés-Ciriano I, Lee JJK, Xi R, Jain D, Jung YL, Yang L, et al. Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing. Nature Genetics. 202010.1038/s41588-019-0576-7", "Analysis of chromothripsis, a mutational process found to occur early in a high proportion of all cancers and to drive tumor genetic heterogeneity", "3.11", "[25]Preprint [87]"], ["Zapatka M, Borozan I, Brewer DS, Iskar M, Grundhoff A, Alawi M, et al. The landscape of viral associations in human cancers. Nature Genetics. 202010.1038/s41588-019-0558-9", "HPV integration and impaired antiviral defense drive cervical, bladder, and head-and-neck carcinomas", "3.12", "[67]Preprint [88]"]], "row_count": 15, "column_count": 4, "numeric_cells": 44, "header_rows": 1, "locator": "section[1]/table-wrap[1]#Tab1"}}, {"id": "asset:table:pmid:34097189:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:1a618aaece15b6e729440d43ee13af73776e8c4ae96f33f8aedd993480e71e35", "bytes": 4285, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[1]/table-wrap[2]#Tab2", "version": null, "note": "Recap of selected datasets and computational tools generated as part of the PCAWG project.", "structure": {"pmcid": "PMC8180541", "section_index": 1, "section_title": "Introduction", "row_count": 16, "column_count": 3}, "table": {"label": "Table 2", "caption": "Recap of selected datasets and computational tools generated as part of the PCAWG project.", "rows": [["Name of datasets and tools", "Description", "Accession link"], ["PCAWG landing page", "This is the recommended starting point for users wishing to access the PCAWG datasets via a single uniform web interface and a high-performance data download client. It provides browsing, download, and usage information for frozen PCAWG data files(Most of the data is open access with some controlled access requiring approval from the ICGC)", "https://dcc.icgc.org/pcawg"], ["Cancer Genome Collaboratory cloud portal", "Cancer Collaboratory is an academic cloud-based access to the PCAWG dataset, excepting the TCGA-originated portion of the controlled data tier (see Bionimbus)(Open and controlled access)", "https://cancercollaboratory.org/"], ["The Bionimbus", "Bionimbus is a cloud portal of protected data for cloud-based access to the TCGA-originated portion of the controlled data tier(Controlled access)", "https://bionimbus-pdc.opensciencedatacloud.org"], ["UCSC Xena data portal", "UCSC Xena is a data portal for visualizations and analyses to integrate omics data generated by the PCAWG Consortium, including copy number, gene expression, gene fusion, promoter usage, simple somatic mutations, large somatic structural variation, mutational signatures, and phenotypic data", "https://pcawg.xenahubs.net"], ["Expression Atlas", "Expression Atlas is an open science resource to find information about gene and protein expression. It enables queries across different tissues, cell types, developmental stages, and experimental conditions, across thousands of publicly available RNA-seq, microarray, and proteomics datasets", "https://www.ebi.ac.uk/gxa/experiments?experimentSet=Pan-Cancer"], ["PCAWG-Scout", "PCAWG-Scout is a data portal that provides a framework to make on-demand, in-depth analyses over the open access PCAWG data", "http://pcawgscout.bsc.es/"], ["Chromothripsis Explorer", "The Chromothripsis Explorer portal enables exploration of patterns of chromothripsis in the PCAWG dataset", "http://compbio.med.harvard.edu/chromothripsis/"], ["Cancer LncRNA Census", "The Cancer LncRNA Census is an ongoing effort to identify and catalogue lncRNA genes which have been causally implicated in cancer", "https://www.gold-lab.org/clc"], ["PCAWG Core Pipelines", "This Dockstore site contains binaries, source code, and documentation for the open source software tool for all core alignment, QC, and variation-calling pipelines used by PCAWG packaged as portable binaries using Docker and described using workflow description languages", "https://dockstore.org/organizations/PCAWG/collections/PCAWG"], ["Overture suite software tool", "Overture comprises a set of open source tools for efficiently managing large genomic datasets and transferring them efficiently and reliably across the Internet", "https://www.overture.bio/"], ["Butler software tool", "Butler is a workflow framework that facilitates large-scale genomic analyses on public and academic clouds while offering comprehensive error detection and self-healing capabilities (reviewed in Suppl. Text Chap. 2.1)", "https://github.com/llevar/butlerl"], ["SVclone software tool", "SVclone is a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole genome sequencing data", "https://github.com/mcmero/SVclone"], ["DriverPower software tool", "DriverPower is a tool used to discover potential coding and non-coding cancer driver elements from tumor whole genome or whole exome somatic mutation sets", "https://github.com/smshuai/DriverPower"], ["TrackSig software tool", "TrackSig is a computational framework to infer changes in somatic mutational signatures over time", "https://github.com/morrislab/TrackSig"], ["ActivePathways tool", "ActivePathways is a tool for multivariate pathway enrichment analysis that identifies gene sets, such as pathways or Gene Ontology terms, prioritizes genes based on the significance of signals from the omics datasets, and performs pathway enrichment analysis of these prioritized genes", "https://github.com/reimandlab/ActivePathways"]], "row_count": 16, "column_count": 3, "numeric_cells": 1, "header_rows": 1, "locator": "section[1]/table-wrap[2]#Tab2"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "PCA"}}, {"id": "method-2", "family": null, "specific": null, "label": "DriverPower", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To accomplish this task, a new software package, DriverPower, combines two state-of-the-art methods, mutational burden and functional impact evidence, to identify a cancer driver [3]."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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RNA-sequencing data were available for 1222 donors."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["pca_fit"], "assets": ["source:pmid:34097189", "asset:table:pmid:34097189:1", "asset:table:pmid:34097189:2"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:30:41Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 3, "usage": {"prompt_tokens": 52877, "completion_tokens": 1719, "cached_tokens": 785}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:34097189", "type": "paper", "label": "pmid:34097189"}, {"id": "source:pmid:34097189", "type": "manuscript", "label": "PMID 34097189 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34097189", "type": "paper", "label": "pmid:34097189"}, {"id": "source:pmid:34097189", "type": "manuscript", "label": "PMID 34097189 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34097189", "type": "extraction", "used": ["source:pmid:34097189"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:34097189", "parent_hash": "sha256:be622b0000e2e2756892f6ee38e017fdc8993221cd73a48b0c9ec8450c390735", "fork_of": null, "generation": 5}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["o 38 different histological cancer subtypes. RNA-sequencing data were available for 1222 donors. The gen", "hondrial DNA mutations, and telomere length. RNA-sequencing data were uniformly processed to call transc", "encing data were uniformly processed to call transcriptomic alterations. Germline variants identified by"], "order_basis": "paper_text_order:2062", "id": "assay:1"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["PCA"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T01:21:02+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}