{"id":"W2315378439","doi":"10.4161/sysb.26326","title":"sbv IMPROVER Diagnostic Signature Challenge","year":2013,"lang":"en","type":"article","venue":"Systems Biomedicine","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Ranking (information retrieval); Computer science; Signature (topology); Machine learning; Data mining; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.02886879,0.002953597,0.002809432,0.003892744,0.002430924,0.004826562,0.004375601,0.003826964,0.01391586],"category_scores_gemma":[0.0624756,0.0005489774,0.002065098,0.00201776,0.001127508,0.002451199,0.006662264,0.00395223,0.01326326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00315548,"about_ca_system_score_gemma":0.005402301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005410714,"about_ca_topic_score_gemma":0.01027172,"domain_scores_codex":[0.9679386,0.01354958,0.001394215,0.003470753,0.01121652,0.0024303],"domain_scores_gemma":[0.9444412,0.01608156,0.001918519,0.01100936,0.02012464,0.006424707],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003058945,0.001501253,0.02300777,0.001082865,0.0007015089,0.00160092,0.0008312622,0.02936324,0.01480808,0.004008279,0.5935494,0.3264865],"study_design_scores_gemma":[0.001683336,0.004114117,0.04575017,0.0003056625,0.0003839788,0.005891876,0.001966799,0.4834104,0.0614646,0.02576457,0.3685968,0.0006677239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5036958,0.005908111,0.1946448,0.0223052,0.01570727,0.005585418,0.06835621,0.09232005,0.09147712],"genre_scores_gemma":[0.6532254,0.0006419009,0.1785989,0.003039063,0.002444376,0.001826732,0.1144918,0.005934567,0.03979725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02886879,"threshold_uncertainty_score":0.1526746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004456227242043943,"score_gpt":0.2024930129204683,"score_spread":0.1980367856784243,"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."}}