{"id":"W4282921784","doi":"10.1101/2022.06.14.495937","title":"Crowd-sourced benchmarking of single-sample tumour subclonal reconstruction","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; Ontario Institute for Cancer Research","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; Vector Institute; Fonds Wetenschappelijk Onderzoek; Vlaamse regering; Fonds De La Recherche Scientifique - FNRS; European Commission; Canadian Institute for Advanced Research; Cancer Research UK; Li Ka Shing Foundation; Prostate Cancer Canada; Genome Canada; Francis Crick Institute; Wellcome Trust; Movember Foundation; Canadian Institutes of Health Research; Google","keywords":"Benchmark (surveying); Benchmarking; Computer science; Sample (material); Code (set theory); Algorithm; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007306775,0.001449804,0.001304074,0.001352438,0.001143619,0.001570482,0.002276111,0.001726446,0.00222843],"category_scores_gemma":[0.01051197,0.0004898232,0.001595232,0.001641528,0.001338884,0.001068964,0.001908202,0.001621578,0.001851037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001441152,"about_ca_system_score_gemma":0.001855602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01347324,"about_ca_topic_score_gemma":0.01017129,"domain_scores_codex":[0.9968837,0.0009656958,0.0001576572,0.0009155375,0.0007356435,0.0003417843],"domain_scores_gemma":[0.9923724,0.003136196,0.0002191794,0.001827595,0.001872978,0.0005717361],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002086719,0.0006255833,0.02718039,0.0008233986,0.0007549693,0.000388695,0.0005652891,0.82336,0.01579055,0.005217663,0.03342499,0.0897817],"study_design_scores_gemma":[0.0001046223,0.0001921487,0.004248295,0.00004280283,0.00003862918,0.00008193222,0.0001639246,0.9709562,0.01389246,0.00350468,0.006731224,0.00004313187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7811241,0.002434761,0.1560587,0.001053629,0.001199,0.0006660088,0.01431899,0.02748096,0.01566386],"genre_scores_gemma":[0.8032271,0.0003376071,0.1550614,0.0004525237,0.0001078975,0.00033949,0.0350011,0.002466357,0.00300645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9926932,"threshold_uncertainty_score":0.03864235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01205659965188284,"score_gpt":0.2098736608730239,"score_spread":0.197817061221141,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}