{"id":"W4242666765","doi":"10.3410/f.1004071.46608","title":"Faculty Opinions recommendation of Development of a virtual screening method for identification of \"frequent hitters\" in compound libraries.","year":2002,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Library Science and Administration","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Identification (biology); Computer science; Virtual screening; Information retrieval; Data mining; World Wide Web; Data science; Chemistry; Stereochemistry; 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":[],"consensus_categories":[],"category_scores_codex":[0.003529825,0.0002752831,0.0007407679,0.0004515041,0.0002871494,0.0001247021,0.001476525,0.0004400059,0.0001851898],"category_scores_gemma":[0.004555544,0.0002075428,0.0003419357,0.002305355,0.0005271656,0.001107099,0.0001974582,0.0003645272,0.000003261162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009009893,"about_ca_system_score_gemma":0.001316091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001472363,"about_ca_topic_score_gemma":0.00004716299,"domain_scores_codex":[0.9947634,0.0006447801,0.002406217,0.000477001,0.001432786,0.0002758182],"domain_scores_gemma":[0.9936933,0.0003506483,0.002725672,0.0006170308,0.0024529,0.0001604464],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001269083,0.0003241354,0.00001434877,0.00240597,0.00004419312,4.100471e-8,0.002228264,6.295519e-7,0.00001515336,0.002401763,0.985214,0.007338789],"study_design_scores_gemma":[0.0004060879,0.00008245662,0.001169172,0.004085076,0.00005225046,0.000001388047,0.0005100477,0.00004868034,0.0002283626,0.00009525925,0.9931326,0.0001886052],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000003422785,0.000277266,0.002666138,0.08442645,0.0004747843,0.001523241,0.9105682,0.00001352403,0.00004696873],"genre_scores_gemma":[0.00009422173,0.0000954138,0.01712621,0.0007463095,0.000110272,0.0001579076,0.9811729,0.00001048448,0.0004863433],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08368015,"threshold_uncertainty_score":0.8463349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07850838774506771,"score_gpt":0.4010542195474273,"score_spread":0.3225458318023595,"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."}}