{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39715685", "kind": "science-record", "revision": 2, "content_hash": "sha256:2b36f3f68c3effe94411d9832d796c012c9c60fa778d5b6c45fb4e4889bd3be8", "created_at": "2026-11-01T20:30:02Z", "updated_at": "2026-11-01T21:34:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "STCGAN: a novel cycle-consistent generative adversarial network for spatial transcriptomics cellular deconvolution.", "doi": "10.1093/bib/bbae670", "pmid": "39715685", "authors": ["Bo Wang", "Yahui Long", "Yuting Bai", "Jiawei Luo", "Chee Keong Kwoh"], "journal": "Briefings in bioinformatics", "year": 2024, "abstract": "MOTIVATION: Spatial transcriptomics (ST) technologies have revolutionized our ability to map gene expression patterns within native tissue context, providing unprecedented insights into tissue architecture and cellular heterogeneity. However, accurately deconvolving cell-type compositions from ST spots remains challenging due to the sparse and averaged nature of ST data, which is essential for accurately depicting tissue architecture. While numerous computational methods have been developed for cell-type deconvolution and spatial distribution reconstruction, most fail to capture tissue complexity at the single-cell level, thereby limiting their applicability in practical scenarios.\nRESULTS: To this end, we propose a novel cycle-consistent generative adversarial network named STCGAN for cellular deconvolution in spatial transcriptomic. STCGAN first employs a cycle-consistent generative adversarial network (CGAN) to pre-train on ST data, ensuring that both the mapping from ST data to latent space and its reverse mapping are consistent, capturing complex spatial gene expression patterns and learning robust latent representations. Based on the learned representation, STCGAN then optimizes a trainable cell-to-spot mapping matrix to integrate scRNA-seq data with ST data, accurately estimating cellular composition within each capture spot and effectively reconstructing the spatial distribution of cells across the tissue. To further enhance deconvolution accuracy, we incorporate spatial-aware regularization that ensures accurate cellular distribution reconstruction within the spatial context. Benchmarking against seven state-of-the-art methods on five simulated and real datasets from various tissues, STCGAN consistently delivers superior cell-type deconvolution performance.\nAVAILABILITY: The code of STCGAN can be downloaded from https://github.com/cs-wangbo/STCGAN and all the mentioned datasets are available on Zenodo at https://zenodo.org/doi/10.5281/zenodo.10799113.", "claims": [{"id": "claim-method", "statement": "For performance evaluation, all experiments are repeated 10 times, and root mean square error (RMSE) and Jensen–Shannon Divergence (JSD) are used to evaluate the cellular deconvolution performance on simulated datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39715685", "locator": "sections[9] \"Experiment settings\" paragraph 3", "quote": "For performance evaluation, all experiments are repeated 10 times, and root mean square error (RMSE) and Jensen–Shannon Divergence (JSD) are used to evaluate the cellular deconvolution performance on simulated datasets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "For the seqFISH+ dataset, both the ST data and the scRNA-seq data are obtained from (https://github.com/CaiGroup/seqFISH-PLUS). The MERFISH dataset is obtained from (https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/), and the scRNA-seq data is obtained from (https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets). The DLPFC dataset is obtained from (http://research.libd.org/spatialLIBD/), and the scRNA-seq data is obtained from (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144136). The mouse brain tissue dataset is collected from (https://mouse.brain-map.org/static/atlas), and the scRNA-seq reference is acquired from (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x). The human breast cancer is collected from (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0), and the scRNA-seq data is obtained from (https://www.immunesinglecell.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39715685", "locator": "sections[20] \"Data availability\" paragraph 2", "quote": "For the seqFISH+ dataset, both the ST data and the scRNA-seq data are obtained from (https://github.com/CaiGroup/seqFISH-PLUS). The MERFISH dataset is obtained from (https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/), and the scRNA-seq data is obtained from (https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets). The DLPFC dataset is obtained from (http://research.libd.org/spatialLIBD/), and the scRNA-seq data is obtained from (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144136). The mouse brain tissue dataset is collected from (https://mouse.brain-map.org/static/atlas), and the scRNA-seq reference is acquired from (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x). The human breast cancer is collected from (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0), and the scRNA-seq data is obtained from (https://www.immunesinglecell.org/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:39715685"], "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": "complete", "blocked_on": null, "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": ["data:geo:GSE144136"], "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": ["data:geo:GSE144136"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39715685", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1093/bib/bbae670", "sha256": "sha256:8010ce9b4e0fcd7bbd47e7e53bc42972594323d15af0bffc624403c73c76440e", "bytes": 67674, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1093/bib/bbae670", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11666287", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE144136", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144136", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE144136", "version": null, "note": "For the seqFISH+ dataset, both the ST data and the scRNA-seq data are obtained from (https://github.com/CaiGroup/seqFISH-PLUS). 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The human breast cancer is collected from (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0), and the scRNA-seq data is obtained from (https://www.immunesinglecell.org/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "For the seqFISH+ dataset, both the ST data and the scRNA-seq data are obtained from (https://github.com/CaiGroup/seqFISH-PLUS). The MERFISH dataset is obtained from (https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/), and the scRNA-seq data is obtained from (https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets). The DLPFC dataset is obtained from (http://research.libd.org/spatialLIBD/), and the scRNA-seq data is obtained from (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144136). 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The human breast cancer is collected from (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0), and the scRNA-seq data is obtained from (https://www.immunesinglecell.org/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4281974", "GSM4281975", "GSM4281976", "GSM4281977", "GSM4281978", "GSM4281979", "GSM4281980", "GSM4281981", "GSM4281982", "GSM4281983", "GSM4281984", "GSM4281985", "GSM4281986", "GSM4281987", "GSM4281988", "GSM4281989", "GSM4281990", "GSM4281991", "GSM4281992", "GSM4281993", "GSM4281994", "GSM4281995", "GSM4281996", "GSM4281997", "GSM4281998", "GSM4281999", "GSM4282000", "GSM4282001", "GSM4282002", "GSM4282003", "GSM4282004", "GSM4282005", "GSM4282006", "GSM4282007"], "sample_count": 34, "group_sizes": null, "group_sizes_basis": null, "platform": "20301", "taxon": "Homo sapiens", "title": "Single-nucleus RNA-seq in the post-mortem brain in major depressive disorder", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE144136 not downloaded", "asset:data:geo:GSE144136 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "For performance evaluation, all experiments are repeated 10 times, and root mean square error (RMSE) and Jensen–Shannon Divergence (JSD) are used to evaluate the cellular deconvolution performance on simulated datasets."}}], "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": "method", "blocked_on": "method:not_determinable", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (root mean square error (RMSE) and Jensen–Shannon Divergence (JSD)), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE144136"], "resolved_accessions": ["GSE144136"], "statements": ["For the seqFISH+ dataset, both the ST data and the scRNA-seq data are obtained from (https://github.com/CaiGroup/seqFISH-PLUS). The MERFISH dataset is obtained from (https://datadryad.org/stash/dataset/doi:10.5061/dryad.8t8s248/), and the scRNA-seq data is obtained from (https://github.com/rdong08/spatialDWLS_dataset/tree/main/datasets). The DLPFC dataset is obtained from (http://research.libd.org/spatialLIBD/), and the scRNA-seq data is obtained from (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE144136). The mouse brain tissue dataset is collected from (https://mouse.brain-map.org/static/atlas), and the scRNA-seq reference is acquired from (https://portal.brain-map.org/atlases-and-data/rnaseq/mouse-whole-cortex-and-hippocampus-10x). The human breast cancer is collected from (https://www.10xgenomics.com/resources/datasets/human-breast-cancer-block-a-section-1-1-standard-1-1-0), and the scRNA-seq data is obtained from (https://www.immunesinglecell.org/)."], "basis": "The paper names GSE144136 in NCBI Gene Expression Omnibus and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:39715685", "data:geo:GSE144136"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE144136"]}, "gaps": ["method:not_determinable"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:43:07Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 18577, "completion_tokens": 3208, "cached_tokens": 256}}, "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:39715685", "type": "paper", "label": "pmid:39715685"}, {"id": "source:pmid:39715685", "type": "manuscript", "label": "PMID 39715685 full text"}, {"id": "data:geo:GSE144136", "type": "dataset", "label": "GEO GSE144136"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39715685", "type": "paper", "label": "pmid:39715685"}, {"id": "source:pmid:39715685", "type": "manuscript", "label": "PMID 39715685 full text"}, {"id": "data:geo:GSE144136", "type": "dataset", "label": "GEO GSE144136"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39715685", "type": "extraction", "used": ["source:pmid:39715685"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE144136", "type": "accession_resolution", "used": ["data:geo:GSE144136"], "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:39715685", "parent_hash": "sha256:655b9bf8a72e6ec65f4960880c21f5bc7b28f198d05449c4f50ed09cf1d2c0cd", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE144136 licence not read", "asset:data:geo:GSE144136 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ble cell-to-spot mapping matrix to integrate scRNA-seq data with ST data, accurately estimating cel", "omprises two primary steps: pre-training and scRNA-seq mapping. In the pre-training stage, a CGAN i", "derlying data distribution (Fig. 1A). In the scRNA-seq mapping stage, we learn a trainable cell-to-", "learn a trainable cell-to-spot matrix to map scRNA-seq data to the predicted ST expression matrix \\"], "order_basis": "paper_text_order:1328", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["on. STCGAN enables horizontal Integration of mouse brain samples The previous discussion has pr", "ated analysis of horizontal samples from the mouse brain anterior and posterior [27, 31]. We fi", "of STCGAN across the anterior and posterior mouse brain samples (Fig. 4A, S4–S5). Spatial scat", "horizontally integrates cell types from the mouse brain anterior and posterior. 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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. 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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}}}