{"id":"W2998817176","doi":"10.29173/hsi240","title":"The Predictive Power of Omics: Clinical Applications","year":2017,"lang":"en","type":"article","venue":"Health Science Inquiry","topic":"Biotechnology and Related Fields","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Omics; Predictive power; Computational biology; Computer science; Data science; Biology; Bioinformatics; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.001656614,0.00004214612,0.0001360742,0.00004358031,0.001374671,0.00001354425,0.0004496738,0.0004753456,0.00000518231],"category_scores_gemma":[0.0002964629,0.00002462397,0.00003817816,0.0001216098,0.007175295,0.0000687976,0.0001128018,0.001002546,0.00002239128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002583668,"about_ca_system_score_gemma":0.0007889143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001340639,"about_ca_topic_score_gemma":0.000002368498,"domain_scores_codex":[0.9991254,0.00001085127,0.000292557,0.000193356,0.0001630525,0.0002148318],"domain_scores_gemma":[0.9985874,0.00006509985,0.0002622582,0.0008533191,0.0001109949,0.0001209678],"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":[0.0005275316,0.0008403924,0.3076687,0.0001495502,0.0001080739,0.000009508894,0.003888093,0.000003841979,0.001028633,0.2348752,0.02037182,0.4305286],"study_design_scores_gemma":[0.0005448161,0.0007715632,0.9457442,0.00008233685,0.00001388126,0.00003848904,0.001205319,0.0003109066,0.0009370696,0.001896609,0.0483963,0.0000585594],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5228532,0.002716596,0.008692014,0.4430972,0.00431095,0.002098136,0.000008188277,0.0001643489,0.01605932],"genre_scores_gemma":[0.9960858,0.001917071,0.0004794342,0.001260438,0.0001010267,0.00001737539,4.181377e-7,0.000002682159,0.0001357872],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6380754,"threshold_uncertainty_score":0.9999254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09174674006042939,"score_gpt":0.4682028423081815,"score_spread":0.3764561022477522,"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."}}