{"id":"W4250106515","doi":"10.1515/iupac.88.0400","title":"Acrosome","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Pesticide and Herbicide Environmental Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Glossary; Terminology; Relation (database); Computer science; Linguistics; Philosophy; Data mining","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":[0.003286316,0.001628234,0.002270265,0.008421085,0.001317677,0.008575057,0.003207965,0.002900426,0.4547079],"category_scores_gemma":[0.03697516,0.001017969,0.001605982,0.01336612,0.0008479333,0.005999482,0.004753731,0.002799281,0.4724816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002044229,"about_ca_system_score_gemma":0.004972061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006884746,"about_ca_topic_score_gemma":0.008306585,"domain_scores_codex":[0.9953148,0.000957245,0.001348034,0.001057425,0.0008773985,0.0004451214],"domain_scores_gemma":[0.9823059,0.005566726,0.002437709,0.00395474,0.004805316,0.0009296272],"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":[0.00004528225,0.000006389582,0.0002371103,0.001290616,0.00001379762,0.00001334901,0.00002124248,0.00004560543,0.0000492754,0.0009605645,0.9935688,0.003747937],"study_design_scores_gemma":[0.0000530603,0.000005280453,0.0005431493,0.0007153218,0.000009633295,0.00002213743,0.00004333101,0.00003496268,0.00005279615,0.0009723228,0.9975371,0.00001087959],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003956487,0.0001922451,0.0001513142,0.0004108053,0.0001751631,0.00004828722,0.9942701,0.0004774911,0.004235],"genre_scores_gemma":[0.0004982429,0.0005155489,0.0008477406,0.0008465583,0.0001090553,0.0004576344,0.9901609,0.0004422282,0.00612205],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4547079,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01550529960843267,"score_gpt":0.3969729802493223,"score_spread":0.3814676806408897,"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."}}