{"id":"W1985833509","doi":"10.1089/cmb.2010.0123","title":"Finding Nearly Optimal GDT Scores","year":2011,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of General Medical Sciences; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computation; Conjecture; Heuristic; Mathematics; Algorithm; Statistics; Combinatorics; Set (abstract data type); Computer science; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003765274,0.00007528596,0.0001530473,0.0001736455,0.00007502363,0.00003710747,0.0005052147,0.00004436597,0.00005052959],"category_scores_gemma":[0.00007168295,0.00005815012,0.00008248223,0.0001423348,0.0000506641,0.0002132991,0.00008217172,0.0002201076,0.00003074449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001400162,"about_ca_system_score_gemma":0.0001027832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009413973,"about_ca_topic_score_gemma":1.999882e-7,"domain_scores_codex":[0.9992074,0.0001060954,0.0003008202,0.0001107483,0.0001324877,0.0001424281],"domain_scores_gemma":[0.9991887,0.0001786951,0.0002996823,0.00007540471,0.0001869035,0.00007058771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00011753,0.0003433342,0.06019208,0.00001729123,0.0002748664,0.0002721673,0.00497754,0.1043579,0.000727566,0.5841779,0.00257571,0.2419661],"study_design_scores_gemma":[0.001711016,0.002631486,0.2484266,0.00006659819,0.00002235473,0.002271099,0.00005239746,0.4290387,0.0005952684,0.3091609,0.005574713,0.0004488869],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1803738,0.000122125,0.8176658,0.0004561911,0.0005722846,0.00001802669,7.907047e-7,0.00001963273,0.0007713768],"genre_scores_gemma":[0.660928,0.00000399012,0.338719,0.000159794,0.0001598591,2.658006e-7,0.000001022429,0.000002602656,0.00002560222],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4805541,"threshold_uncertainty_score":0.2371293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0318057403196908,"score_gpt":0.2887815079475446,"score_spread":0.2569757676278538,"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."}}