{"id":"W2950256199","doi":"10.1101/254839","title":"Valection: Design Optimization for Validation and Verification Studies","year":2018,"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":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada; Government of Ontario; Canadian Institutes of Health Research; Genome Canada; National Cancer Institute; National Institutes of Health; Ontario Institute for Cancer Research; Movember Foundation","keywords":"Computer science; Benchmarking; Selection (genetic algorithm); Inference; Set (abstract data type); Data mining; Replicate; Ground truth; Software; Throughput; Machine learning; Artificial intelligence; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.01160223,0.002025434,0.001023959,0.001089178,0.0004602394,0.001373828,0.001650622,0.0008230496,0.006949562],"category_scores_gemma":[0.01719786,0.0009171898,0.001627327,0.0005906195,0.0009445524,0.0008727849,0.001489715,0.001637803,0.001425132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008678355,"about_ca_system_score_gemma":0.002125026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001148917,"about_ca_topic_score_gemma":0.00128948,"domain_scores_codex":[0.9952537,0.002455168,0.0002948678,0.000562204,0.001156296,0.0002778705],"domain_scores_gemma":[0.9895387,0.007218691,0.0007947159,0.001045518,0.001223258,0.0001790784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001364142,0.0003449105,0.008231099,0.001127364,0.0004509561,0.0004756958,0.0003373502,0.5872293,0.04726286,0.02337481,0.01213839,0.3176632],"study_design_scores_gemma":[0.0001727301,0.0004323844,0.0008211755,0.00009908238,0.0000920219,0.0001367625,0.00004631629,0.9404976,0.03377942,0.01035339,0.01351845,0.00005068129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01117675,0.0001509346,0.9789478,0.00007785403,0.00003210187,0.0002358864,0.0002093205,0.007762698,0.00140677],"genre_scores_gemma":[0.1515405,0.0001445684,0.8419464,0.0001683877,0.00002595528,0.001281745,0.0006908888,0.002570714,0.001630723],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01160223,"threshold_uncertainty_score":0.06135917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03061296802386692,"score_gpt":0.2610822873949116,"score_spread":0.2304693193710446,"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."}}