{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001119788,0.0009326703,0.001202567,0.0005618345,0.0001602138,0.0001192054,0.001188612,0.0007031321,0.03106888],"category_scores_gemma":[0.002095353,0.0006892049,0.0003357965,0.0004332,0.0003530696,0.0001991088,0.0004198955,0.0007660721,0.0005561337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001724968,"about_ca_system_score_gemma":0.00194019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001667116,"about_ca_topic_score_gemma":0.001678508,"domain_scores_codex":[0.9942358,0.0002092867,0.0008355771,0.0009504757,0.002825961,0.0009429],"domain_scores_gemma":[0.9955142,0.0002016195,0.000660382,0.002386075,0.0008497723,0.0003879748],"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":[0.0001914334,0.0002901456,0.000002535924,0.000188183,0.0002278742,0.0001887178,0.000005899915,3.959676e-7,0.00007428849,0.000007210308,0.9977227,0.001100626],"study_design_scores_gemma":[0.001220332,0.0002226039,0.00002362566,0.001250349,0.0002366644,0.00005360788,0.000007801944,0.000001780413,0.0000439126,0.000141137,0.9958829,0.0009153431],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003809363,0.0008856285,0.00003415166,0.0003272647,0.001174617,0.0005166662,0.996421,0.0004242059,0.0001784015],"genre_scores_gemma":[0.000003790347,0.0004113766,0.00007389035,0.000228886,0.002498312,0.00003241041,0.9956782,0.0002767756,0.0007963009],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03051274,"threshold_uncertainty_score":0.9995559,"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."}}