{"id":"W2949665991","doi":"10.1101/090589","title":"Mining human prostate cancer datasets: The “camcAPP” shiny app","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"","keywords":"Prostate cancer; Computer science; Resource (disambiguation); Outcome (game theory); Cancer; Data science; Computational biology; Biology; Genetics","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":[0.00199659,0.00201119,0.001391104,0.003321462,0.0007319797,0.002039815,0.002672711,0.001348866,0.06850861],"category_scores_gemma":[0.008437856,0.001117556,0.001760769,0.003213963,0.0003882879,0.001265991,0.004004513,0.001745511,0.03264939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004765592,"about_ca_system_score_gemma":0.001374822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003455431,"about_ca_topic_score_gemma":0.00896576,"domain_scores_codex":[0.9989367,0.0002084005,0.00009911098,0.000272208,0.0004195581,0.00006407721],"domain_scores_gemma":[0.9973481,0.001551504,0.0001118397,0.0005538109,0.0002438494,0.000190889],"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":[0.0003659659,0.00007759689,0.001335636,0.0008571644,0.0002714557,0.0004177426,0.0001418972,0.0007653175,0.002096698,0.001816836,0.9366741,0.0551795],"study_design_scores_gemma":[0.001140395,0.0001622662,0.01216656,0.0005411388,0.0001972339,0.001317654,0.0002637499,0.0354696,0.01101795,0.02766699,0.9098507,0.0002057335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.006057803,0.003075294,0.09387084,0.002508776,0.0007040254,0.001040614,0.4893426,0.3864921,0.01690802],"genre_scores_gemma":[0.0407338,0.002478408,0.2884297,0.003497038,0.0006626649,0.006478983,0.5959539,0.0443943,0.01737112],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06850861,"threshold_uncertainty_score":0.2291842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755905544459465,"score_gpt":0.317612200832246,"score_spread":0.2800531453876513,"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."}}