{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001761713,0.0001822423,0.0002030097,0.00005211806,0.00005632844,0.00003160273,0.0001916404,0.0002716195,0.00007328854],"category_scores_gemma":[0.00006019651,0.0001339456,0.00005719992,0.00009298866,0.00008347848,0.000004956282,0.00008531733,0.0001241883,0.0002043871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001419229,"about_ca_system_score_gemma":0.00003142496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001297473,"about_ca_topic_score_gemma":0.000008231455,"domain_scores_codex":[0.9989632,0.00002536236,0.0003137699,0.0002336443,0.0001509795,0.0003130087],"domain_scores_gemma":[0.9991999,0.00002864003,0.0001128336,0.0004087495,0.00009628431,0.0001535829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003685949,0.0001759814,0.0006140986,0.0005939676,0.0003611656,0.00001797274,0.0005328197,0.00007347257,0.3843591,0.001347037,0.5681779,0.04370965],"study_design_scores_gemma":[0.003292352,0.002127167,0.003566754,0.0003532372,0.00008511372,0.0001373135,0.001414135,0.00471453,0.003580904,0.0005357948,0.979144,0.001048674],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7512983,0.1204084,0.0264896,0.008563331,0.01423333,0.007438579,0.0001956655,0.0002698993,0.07110281],"genre_scores_gemma":[0.9937786,0.0003934494,0.00009101771,0.0004431262,0.001687553,0.00007567009,0.0001971414,0.00002393891,0.003309516],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4109661,"threshold_uncertainty_score":0.5462141,"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."}}