{"id":"W4235182567","doi":"10.1515/iupac.78.0490","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 Guelph","funders":"","keywords":"Glossary; Population; Relation (database); Computer science; Management science; Data science; Engineering; Data mining; Environmental health; Medicine","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009095512,0.0006309619,0.0008307675,0.0005090937,0.0001325064,0.0000853149,0.0005926804,0.0005940751,0.01035279],"category_scores_gemma":[0.001185494,0.0004828707,0.0002280142,0.0003406171,0.0001111833,0.0001981985,0.0002081134,0.0005336968,0.0001598908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001643373,"about_ca_system_score_gemma":0.0005747451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006633648,"about_ca_topic_score_gemma":0.003744968,"domain_scores_codex":[0.9956147,0.0001783286,0.0006669982,0.0007310134,0.002196221,0.000612741],"domain_scores_gemma":[0.996959,0.00008840181,0.000561488,0.001514217,0.0006431467,0.000233791],"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":[0.0002258933,0.0001620238,0.00004611438,0.00008533082,0.00009626219,0.00005009619,0.000002496447,0.000001442356,0.0000139791,0.00001127863,0.9970697,0.002235438],"study_design_scores_gemma":[0.00105118,0.000119334,0.0006694286,0.0004813729,0.0001805472,0.00001770469,0.000003497661,0.000002818851,0.000004441477,0.000344771,0.996489,0.0006359089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001279787,0.0003587824,0.00002795532,0.0002018717,0.001092228,0.0004032496,0.9974549,0.0002880171,0.00004495234],"genre_scores_gemma":[0.00002603131,0.0001626695,0.00004732655,0.0001228423,0.002035677,0.000016625,0.9970112,0.0001851834,0.0003923837],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0101929,"threshold_uncertainty_score":0.9997623,"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."}}