{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003230065,0.0004962194,0.0005955845,0.00003636366,0.0006136867,0.0001058031,0.0009724218,0.0002992123,0.07270508],"category_scores_gemma":[0.0002564633,0.0004327812,0.0001735941,0.00006097402,0.0007957625,0.0001800523,0.001420866,0.0005370291,0.0002844808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008344125,"about_ca_system_score_gemma":0.00004482885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001908186,"about_ca_topic_score_gemma":0.006755822,"domain_scores_codex":[0.9971085,0.00003858425,0.0003552557,0.0006724296,0.00125366,0.0005715554],"domain_scores_gemma":[0.9982047,0.00004058861,0.0003241983,0.001219988,0.00001104807,0.0001994403],"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":[0.00003098083,0.0001679915,0.0008898356,0.00002354284,0.00004711933,0.0001292325,0.00001245975,0.00001618507,0.00003053813,3.159037e-7,0.9967115,0.001940329],"study_design_scores_gemma":[0.0003650998,0.0001327864,0.01288374,0.00007266068,0.0001054354,0.0000141223,0.00001480236,0.000002563065,0.00001358593,0.0001695808,0.9857326,0.0004930599],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001197578,0.0004946662,0.000009631949,0.0003357819,0.0004566537,0.0002337225,0.9963272,0.00004114347,0.000903649],"genre_scores_gemma":[0.0001814522,0.00110027,0.00006462142,0.0005109801,0.0003947402,0.00002134752,0.9959336,0.00003376413,0.001759207],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0724206,"threshold_uncertainty_score":0.9998124,"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."}}