{"id":"W2802403307","doi":"10.1007/978-3-319-78196-9_5","title":"Timelines of Prostate Cancer Biomarkers","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Prostate cancer; Disease; Prostate-specific antigen; Popularity; Biomarker; Timeline; Biomarker discovery; Cancer biomarkers; Medicine; Cancer; Computational biology; Bioinformatics; Oncology; Gene; Internal medicine; Biology; Proteomics; Psychology; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001577209,0.0003527352,0.0007282412,0.0001819734,0.00008643947,0.00001236445,0.0001164212,0.0008207294,0.001680537],"category_scores_gemma":[0.00003395367,0.0002744372,0.0002334565,0.0001479332,0.0005357891,0.0000230474,0.00007328963,0.0006596224,0.0000136238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000273741,"about_ca_system_score_gemma":0.0003270022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009752458,"about_ca_topic_score_gemma":0.0002754361,"domain_scores_codex":[0.9984319,0.00002485673,0.0003849933,0.0003882326,0.0003577166,0.0004122676],"domain_scores_gemma":[0.9991032,0.0001267438,0.00021484,0.0002077355,0.000263574,0.00008387233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0104971,0.0002740035,0.02266487,0.001756263,0.004737018,0.0005386294,0.005669326,0.0007731903,0.00036447,0.0007523152,0.06763648,0.8843364],"study_design_scores_gemma":[0.0323144,0.004990097,0.008894416,0.02141732,0.004920749,0.00007467955,0.0001009956,0.008221772,0.006132815,0.1455657,0.7617677,0.00559932],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01357736,0.3258204,0.003703219,0.01950825,0.006512571,0.01255669,0.001122098,0.000554436,0.6166449],"genre_scores_gemma":[0.5503832,0.04776489,0.001415455,0.002269064,0.02912206,0.0003632028,0.002176439,0.0007916821,0.3657141],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.878737,"threshold_uncertainty_score":0.9999708,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02446875217537279,"score_gpt":0.3257551131412517,"score_spread":0.3012863609658789,"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."}}