{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002522625,0.0007861557,0.0005981928,0.003729836,0.0006065562,0.003779595,0.0007717023,0.0008362082,0.04038801],"category_scores_gemma":[0.02763882,0.0005188381,0.0005581396,0.006361183,0.0003684026,0.004155938,0.0008460117,0.001271599,0.01450308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00165023,"about_ca_system_score_gemma":0.0008095758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005651938,"about_ca_topic_score_gemma":0.006546136,"domain_scores_codex":[0.9978366,0.0004562117,0.0001389781,0.0004062851,0.001028428,0.0001335119],"domain_scores_gemma":[0.9843143,0.009245872,0.001874206,0.001061305,0.003033059,0.0004711106],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0005799086,0.00003901486,0.01736331,0.0007673333,0.0001254906,0.0001375719,0.0005437683,0.01467344,0.001622284,0.1159755,0.3081468,0.5400255],"study_design_scores_gemma":[0.00005437199,0.0002586078,0.0466043,0.0008707088,0.0001692918,0.0008020653,0.001034597,0.04778627,0.004656501,0.1031464,0.7944785,0.0001383361],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.08413209,0.1363744,0.1662237,0.03429838,0.01684195,0.0001463029,0.108568,0.007873544,0.4455417],"genre_scores_gemma":[0.5786914,0.04995858,0.0756485,0.002709689,0.006782962,0.0003062994,0.0479816,0.00436957,0.2335514],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.04038801,"threshold_uncertainty_score":0.1351114,"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."}}