{"id":"W2060552609","doi":"10.1016/j.procs.2012.04.180","title":"Kepler for ‘Omics Bioinformatics","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Omics; Kepler; Bioinformatics; Data science; Computational biology; Biology","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01210193,0.0001358825,0.0001747101,0.0005056189,0.0005603907,0.001341561,0.002664651,0.00002799905,0.00002216182],"category_scores_gemma":[0.002054866,0.00009771012,0.00007717597,0.002246391,0.0003746209,0.002139327,0.001227317,0.00006794642,0.0005311641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005067786,"about_ca_system_score_gemma":0.000156063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001842866,"about_ca_topic_score_gemma":6.459891e-7,"domain_scores_codex":[0.9963428,0.000018092,0.0005676642,0.0005884888,0.001724179,0.0007587173],"domain_scores_gemma":[0.9971573,0.0006657256,0.0002226223,0.001077032,0.0005506299,0.0003266348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001017045,0.0001569418,0.01643309,0.00002641343,0.000006800821,3.231433e-7,0.003969477,0.0008671719,0.0001453098,0.02209961,0.1245117,0.8317729],"study_design_scores_gemma":[0.0002272875,0.000052041,0.009919033,0.000007955104,0.000005686695,0.00001163539,0.0001617066,0.8242251,0.0005977336,0.005799604,0.1587568,0.0002353644],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03935815,0.00003655013,0.9516473,0.0004584924,0.006109799,0.0003392362,0.00001397166,0.0001053911,0.001931102],"genre_scores_gemma":[0.5180892,0.000001659348,0.4797098,0.001032513,0.0006598109,0.00001822503,0.000005211885,0.000007004365,0.0004766131],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8315376,"threshold_uncertainty_score":0.9996951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1622882265160856,"score_gpt":0.3913130239518328,"score_spread":0.2290247974357471,"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."}}