{"id":"W4236116525","doi":"10.1515/iupac.81.0029","title":"Acute","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; Relation (database); Environmental risk assessment; Computer science; Ecology; Risk assessment; Biology; Data mining; 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":[0.001533675,0.001696961,0.00144427,0.003556454,0.001306541,0.004084957,0.002868356,0.002167202,0.2171447],"category_scores_gemma":[0.01401022,0.0006213306,0.001619442,0.006182513,0.0004877037,0.003575101,0.003100543,0.002321795,0.2404979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002381178,"about_ca_system_score_gemma":0.003405149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01903056,"about_ca_topic_score_gemma":0.04344376,"domain_scores_codex":[0.997178,0.0005072782,0.0004112474,0.0008955619,0.0006350013,0.0003729358],"domain_scores_gemma":[0.9943542,0.00128948,0.0005875455,0.001283019,0.002065559,0.0004200314],"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":[0.00009623971,0.00002005606,0.001475739,0.0007460555,0.00001898026,0.00002344554,0.0000312622,0.0001039967,0.00006088454,0.001243368,0.9907611,0.005418737],"study_design_scores_gemma":[0.00009347151,0.00001532248,0.002718633,0.000514524,0.00001783988,0.00006557046,0.0001204913,0.0001232237,0.0001260206,0.001650595,0.9945353,0.00001899549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002051246,0.0001902909,0.0001333282,0.0002426058,0.00008147935,0.0000447709,0.9947422,0.000256329,0.004103888],"genre_scores_gemma":[0.000610591,0.0001700154,0.000450327,0.0002918372,0.00002555023,0.0001902329,0.9949444,0.00007967079,0.003237361],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7828553,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01468001975759597,"score_gpt":0.4308624982053531,"score_spread":0.4161824784477571,"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."}}