{"id":"W4247338476","doi":"10.1515/iupac.81.0705","title":"Population","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Ecotoxicology; Population; Relation (database); Ecology; Computer science; Biology; Environmental health; Data mining; Medicine; Philosophy; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001587946,0.001198764,0.001227158,0.002475718,0.0008344662,0.002714832,0.00284078,0.001895044,0.1714235],"category_scores_gemma":[0.01359627,0.0005348389,0.001359497,0.005121129,0.0003246998,0.002229164,0.001975745,0.002344737,0.1401046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00153347,"about_ca_system_score_gemma":0.002514173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02267668,"about_ca_topic_score_gemma":0.02856679,"domain_scores_codex":[0.9979919,0.0003728734,0.0002460963,0.0007531036,0.0003953718,0.0002406382],"domain_scores_gemma":[0.9965279,0.0009005226,0.000344924,0.0007326155,0.001301365,0.000192646],"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":[0.0001014885,0.00001887522,0.003953656,0.0004651623,0.00004264694,0.00002515613,0.00003678279,0.0001921862,0.00003747619,0.001470321,0.9840453,0.009611025],"study_design_scores_gemma":[0.0002192703,0.00002428255,0.007087669,0.0006125962,0.00005772709,0.000138961,0.0001910866,0.0005364358,0.0001526384,0.004325609,0.9866195,0.00003416451],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003632496,0.0001518692,0.0002746793,0.0003048314,0.00009008014,0.00005812628,0.9952857,0.0001972724,0.00327425],"genre_scores_gemma":[0.002105309,0.0002503642,0.001005211,0.0004755601,0.00005706599,0.0005127033,0.9901035,0.0001017141,0.005388543],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8285766,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168254573769787,"score_gpt":0.4305858230236916,"score_spread":0.4137603656467129,"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."}}