{"id":"W2074909247","doi":"10.1021/ci3000216","title":"Harvesting Classification Trees for Drug Discovery","year":2012,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Acadia University; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Drug discovery; Computer science; Computational biology; Biology; Bioinformatics","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":[0.0008719153,0.00006197157,0.0001062156,0.00009924985,0.00004429562,0.0002251722,0.0001639555,0.00002714941,3.909548e-7],"category_scores_gemma":[0.0002975494,0.00005149371,0.0000607952,0.00008284944,0.0000114807,0.009021102,0.00005090169,0.00009238161,7.874045e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003656635,"about_ca_system_score_gemma":0.00005407431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001362709,"about_ca_topic_score_gemma":5.563028e-8,"domain_scores_codex":[0.9991522,0.00002091284,0.0004751601,0.00004048523,0.0001912357,0.0001199599],"domain_scores_gemma":[0.9991407,0.0002138702,0.0002921368,0.00006566382,0.0002005041,0.00008705849],"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":[0.0001159064,0.0001361983,0.0004827875,0.0001791404,0.00006213787,2.295809e-7,0.009011976,0.2912346,0.0261369,0.2650428,0.001137401,0.4064599],"study_design_scores_gemma":[0.0002595262,0.000008010741,0.00007945889,0.00002853878,0.00000665567,0.00002704633,0.0001269154,0.9908838,0.00346485,0.004235238,0.0008143064,0.00006563761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2734987,0.00008238461,0.7256119,0.0004727183,0.0001470194,0.00004041965,9.295989e-7,0.000009077129,0.0001368629],"genre_scores_gemma":[0.8107587,0.00000980319,0.1888887,0.000198378,0.0001308356,0.000002147341,0.000003270363,0.00000196287,0.000006254853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6996493,"threshold_uncertainty_score":0.6540081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06652699839187372,"score_gpt":0.3255266103784071,"score_spread":0.2589996119865334,"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."}}