{"id":"W2035820679","doi":"10.1586/epr.10.95","title":"A synopsis of the 3rd annual Cancer Proteomics Conference","year":2010,"lang":"en","type":"article","venue":"Expert Review of Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Proteomics; Biomarker discovery; Data science; Proteogenomics; Library science; Computer science; Biology; Genomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002283001,0.0002262212,0.0004778104,0.00002137567,0.00008878345,0.000008676773,0.0008828564,0.0001677362,0.0005943044],"category_scores_gemma":[0.000230315,0.0001618166,0.0002297949,0.0002085858,0.000288836,0.00009396042,0.000239733,0.0005099054,0.000002929615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004126608,"about_ca_system_score_gemma":0.0002507155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001173858,"about_ca_topic_score_gemma":0.00002688852,"domain_scores_codex":[0.9985384,0.0000210863,0.0006692145,0.0003067588,0.0002429964,0.0002215209],"domain_scores_gemma":[0.9977011,0.00003478654,0.0007185824,0.001077829,0.0003994647,0.00006822077],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001366314,0.00007359504,0.0002005715,0.002246846,0.00001858483,1.644971e-7,0.0001399508,0.000001075598,0.9876087,0.004987645,0.001165811,0.003543422],"study_design_scores_gemma":[0.0001378079,0.00001488969,0.00001777681,0.00344943,0.00002317112,0.000007316397,0.00003357054,0.00006521625,0.9667345,0.001550872,0.02777047,0.0001949976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7343951,0.0615745,0.1376404,0.01993778,0.000919545,0.01733577,0.003100718,0.0005946056,0.02450157],"genre_scores_gemma":[0.09377452,0.06771382,0.8303456,0.0008734947,0.0003297649,0.005668114,0.00002286852,0.00008533882,0.00118649],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6927052,"threshold_uncertainty_score":0.6598688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0128402301156626,"score_gpt":0.3270164925651696,"score_spread":0.314176262449507,"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."}}