{"id":"W4247193665","doi":"10.1515/iupac.83.0435","title":"Plate Reader","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Glossary; Field (mathematics); Context (archaeology); Process (computing); Computer science; Multidisciplinary approach; Data science; Component (thermodynamics); Management science; Engineering; Sociology; Linguistics; Biology; Mathematics; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001362397,0.002124135,0.001656136,0.004281938,0.0008493679,0.003752917,0.003757582,0.00143953,0.2837959],"category_scores_gemma":[0.007844996,0.0007748662,0.001575427,0.006504541,0.000428763,0.002710669,0.002825354,0.002462684,0.4717387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001517899,"about_ca_system_score_gemma":0.002196024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01321034,"about_ca_topic_score_gemma":0.0286049,"domain_scores_codex":[0.9982715,0.0002590208,0.0002734216,0.0005096158,0.0004807888,0.0002056474],"domain_scores_gemma":[0.9955125,0.0007109679,0.0003300605,0.001571819,0.001486965,0.0003876026],"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":[0.00002904487,0.000009325776,0.0002437867,0.000206969,0.000006004813,0.00000679605,0.000007560473,0.00007634555,0.00004075818,0.0003104147,0.9956692,0.003393684],"study_design_scores_gemma":[0.00005180651,0.000009958819,0.001307965,0.0001728484,0.000007056421,0.00004167227,0.00005039883,0.0002111269,0.0002394045,0.001103995,0.9967891,0.00001481521],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001108917,0.00008276907,0.0003225906,0.0001003948,0.00007887838,0.00004040029,0.9922993,0.001582085,0.005382769],"genre_scores_gemma":[0.000277061,0.00007911847,0.0005976685,0.0001187749,0.00001655209,0.00008565588,0.9949325,0.0002753328,0.003617432],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2837959,"threshold_uncertainty_score":0.9493918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02567590193249468,"score_gpt":0.4320207778990955,"score_spread":0.4063448759666008,"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."}}