{"id":"W4254852465","doi":"10.1515/iupac.79.0866","title":"Astringent","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; Chemical nomenclature; Computer science; Toxicology; Chemistry; Philosophy; Linguistics; Biology; Organic chemistry","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":[0.0009953186,0.001852718,0.001774137,0.003593744,0.0008581628,0.004090086,0.002114858,0.001542268,0.1862334],"category_scores_gemma":[0.007529572,0.00068178,0.001776749,0.006909486,0.0003642187,0.00214359,0.002367414,0.001896962,0.2689913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00133943,"about_ca_system_score_gemma":0.002445763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01633485,"about_ca_topic_score_gemma":0.03545822,"domain_scores_codex":[0.9985519,0.0002315049,0.000219004,0.0005116226,0.0003121092,0.0001737724],"domain_scores_gemma":[0.9974993,0.0006038252,0.0004161879,0.0006263487,0.0006224098,0.0002319843],"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":[0.0001343645,0.00001230713,0.0009657047,0.001041605,0.00004023815,0.00001762632,0.00002126726,0.0001280253,0.00007714751,0.0006523159,0.9900895,0.006819883],"study_design_scores_gemma":[0.0001268521,0.00001277475,0.002119121,0.0005619089,0.00003551422,0.00005595538,0.0000485092,0.000130589,0.0001427192,0.001171887,0.9955769,0.00001735112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00009641289,0.0001759734,0.0000721098,0.0000783217,0.00003054597,0.00001113738,0.9973785,0.0002902518,0.001866795],"genre_scores_gemma":[0.0003881034,0.0002106122,0.000305963,0.000150613,0.0000160596,0.00006980327,0.9966281,0.0001306418,0.002100112],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8137667,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01879521266228537,"score_gpt":0.4218275287785853,"score_spread":0.4030323161162999,"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."}}