{"id":"W4232093027","doi":"10.1515/iupac.81.0435","title":"Hard Water","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":[0.0006370382,0.001378707,0.001117916,0.003416022,0.0009103501,0.002734535,0.002136476,0.001345707,0.1717963],"category_scores_gemma":[0.00516323,0.0005744047,0.001053997,0.0084311,0.0003662507,0.002784646,0.002556202,0.001527442,0.2094643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001463907,"about_ca_system_score_gemma":0.002131519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03683169,"about_ca_topic_score_gemma":0.07092457,"domain_scores_codex":[0.9988896,0.0001507724,0.0001293944,0.0003546362,0.0003123312,0.0001632331],"domain_scores_gemma":[0.9978144,0.0004123811,0.000257372,0.0005120992,0.0008111975,0.000192402],"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":[0.00004330292,0.00001104044,0.0009494942,0.0005011336,0.00001460315,0.00001691245,0.00002445066,0.0001290122,0.00006560231,0.0007055258,0.9912668,0.006272112],"study_design_scores_gemma":[0.00003993489,0.00000575292,0.002685925,0.0002592879,0.000007373051,0.00002605755,0.0001101335,0.000138633,0.0001220807,0.00103325,0.9955577,0.00001388305],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001262374,0.00006716002,0.00006742616,0.00007810346,0.00002624505,0.000008883078,0.9972178,0.0001941678,0.002214067],"genre_scores_gemma":[0.0003748369,0.00008216629,0.000210988,0.00006701339,0.000006706002,0.00003479999,0.9967282,0.00007308718,0.002422091],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8282037,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861358714682724,"score_gpt":0.4037082466834178,"score_spread":0.3850946595365906,"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."}}