{"id":"W4249518147","doi":"10.1515/iupac.76.0409","title":"Topical Effect","year":2016,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"History and advancements in chemistry","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Glossary; Toxicokinetics; Relation (database); Hazard; Toxicology; Computer science; Medicine; Chemistry; Pharmacology; Data mining; Biology; Linguistics; Philosophy","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":[0.001169139,0.002035622,0.001546643,0.006158651,0.00124005,0.004759207,0.001944103,0.001807366,0.1920817],"category_scores_gemma":[0.009580673,0.0006888466,0.001908145,0.007355645,0.0004741995,0.004055833,0.003122456,0.002067654,0.2591598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674349,"about_ca_system_score_gemma":0.0032712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01061279,"about_ca_topic_score_gemma":0.02385459,"domain_scores_codex":[0.997969,0.0002998966,0.0003486246,0.0006759067,0.0004803306,0.0002262504],"domain_scores_gemma":[0.9955167,0.001265883,0.0005046146,0.001208783,0.00110528,0.0003987388],"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":[0.00004877433,0.00001304713,0.0006472833,0.001023266,0.00001832002,0.00002047856,0.00002797124,0.00007107113,0.0001658121,0.0007068677,0.9911277,0.006129354],"study_design_scores_gemma":[0.00003004699,0.000006610142,0.001426375,0.0003033718,0.00001314825,0.00004369672,0.00004560955,0.0000641055,0.000148765,0.0006814045,0.9972242,0.0000125082],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001258411,0.0003099518,0.0001288993,0.0001393723,0.00009778641,0.00002695412,0.9951465,0.0006268954,0.00339787],"genre_scores_gemma":[0.0003808115,0.0002787915,0.0004328734,0.0001891025,0.00002927527,0.00008852404,0.9952971,0.0002116307,0.003092001],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1920817,"threshold_uncertainty_score":0.6425774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009241259105270825,"score_gpt":0.3887253654760141,"score_spread":0.3794841063707432,"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."}}