{"id":"W4251564383","doi":"10.1515/iupac.79.1005","title":"Chromatid","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; 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":[],"consensus_categories":[],"category_scores_codex":[0.001470726,0.002368141,0.002034777,0.005471957,0.001226989,0.004019413,0.0034328,0.002510365,0.2155966],"category_scores_gemma":[0.01033239,0.000894236,0.002013923,0.008007356,0.000426115,0.002671852,0.002778455,0.002098017,0.275854],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002187407,"about_ca_system_score_gemma":0.004324347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02471601,"about_ca_topic_score_gemma":0.04206039,"domain_scores_codex":[0.9980101,0.0002994802,0.0003684253,0.0006690591,0.0004331818,0.0002197638],"domain_scores_gemma":[0.9949981,0.001175253,0.0005197436,0.001328981,0.00163357,0.000344383],"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":[0.00009754975,0.00001588488,0.0007777992,0.001474926,0.00004509783,0.00001612701,0.00001912391,0.0001746395,0.0001152599,0.0005786509,0.990919,0.005765989],"study_design_scores_gemma":[0.0001581036,0.00001393266,0.001929165,0.0005208827,0.00004532018,0.00004264857,0.00004300075,0.0001723912,0.0002380773,0.001339687,0.9954723,0.00002450777],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003820885,0.00007406923,0.00007082038,0.00005785409,0.00001828348,0.00001650874,0.9984554,0.0002972082,0.0009716978],"genre_scores_gemma":[0.0001925099,0.0001054011,0.0003409623,0.0001062491,0.000009809041,0.00009038077,0.9980199,0.00008793737,0.001046821],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2155966,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01529615787103004,"score_gpt":0.4192704885237665,"score_spread":0.4039743306527365,"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."}}