{"id":"W4232258419","doi":"10.1515/iupac.79.0894","title":"Benefit","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; Biology; 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":[],"consensus_categories":[],"category_scores_codex":[0.001378495,0.001364635,0.001375334,0.002992106,0.001071779,0.003657958,0.002213112,0.00187865,0.2690326],"category_scores_gemma":[0.01596296,0.0004912604,0.001806165,0.005339585,0.0002846078,0.002803,0.002329175,0.001626038,0.2531009],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00150664,"about_ca_system_score_gemma":0.003016545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01906355,"about_ca_topic_score_gemma":0.03080076,"domain_scores_codex":[0.9979373,0.0003618643,0.0003284243,0.0007074592,0.0004254319,0.0002394783],"domain_scores_gemma":[0.9944879,0.001521655,0.0005554271,0.00137008,0.001701443,0.0003634644],"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":[0.0001254491,0.00001564846,0.001601494,0.0009448763,0.00004490121,0.00001533523,0.00002095541,0.0001193835,0.00005319214,0.001033377,0.9853809,0.01064445],"study_design_scores_gemma":[0.0001350876,0.00001513154,0.002952675,0.0006477963,0.00005001632,0.00005681744,0.00007358546,0.0001352726,0.0001062278,0.001976673,0.9938326,0.00001810894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001552172,0.0001992441,0.0001279394,0.000195203,0.00006700678,0.00003227148,0.9946862,0.0002521073,0.004284781],"genre_scores_gemma":[0.0009584267,0.0002475917,0.0005422111,0.0005379549,0.00003986156,0.0001850537,0.9923801,0.0001509512,0.004957953],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2690326,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01613022108435774,"score_gpt":0.4123548776291683,"score_spread":0.3962246565448106,"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."}}