{"id":"W4250296034","doi":"10.1515/iupac.76.0310","title":"Mulliken Population Analysis","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Population; Computer science; Toxicology; Medicine; Environmental health; Pharmacology; Data mining; Biology; Linguistics","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":[0.001721095,0.001042455,0.00100157,0.003221988,0.0006316226,0.001751826,0.002190069,0.001022458,0.08579163],"category_scores_gemma":[0.01199378,0.0004469187,0.001478639,0.004956661,0.000295234,0.001044792,0.001684337,0.0016381,0.06597102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009379529,"about_ca_system_score_gemma":0.001582597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585792,"about_ca_topic_score_gemma":0.03022442,"domain_scores_codex":[0.9987714,0.000297234,0.000169614,0.0004233322,0.0002204177,0.0001179775],"domain_scores_gemma":[0.9966259,0.001157979,0.0003460047,0.0009823696,0.0006820587,0.0002056827],"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":[0.00009413253,0.00003701219,0.005448823,0.000464056,0.00008463881,0.00003914638,0.00004937837,0.0004996326,0.00006895354,0.001078238,0.9825984,0.009537384],"study_design_scores_gemma":[0.0002836444,0.00004358162,0.01860634,0.000415604,0.00007505629,0.0001374714,0.0001775191,0.001527913,0.0002278956,0.003676508,0.9747843,0.00004409202],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005987934,0.00009608307,0.000534807,0.0000690284,0.00004481204,0.00003817484,0.9970606,0.0004361787,0.001121394],"genre_scores_gemma":[0.001562385,0.00006509784,0.001194457,0.00006413567,0.00001970181,0.0002871572,0.9949741,0.0001187489,0.00171415],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08579163,"threshold_uncertainty_score":0.2870016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02742321062929971,"score_gpt":0.4496711910890233,"score_spread":0.4222479804597236,"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."}}