{"id":"W4230549876","doi":"10.1515/iupac.79.1921","title":"Regulatory Sequence","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; Chemical nomenclature; Computer science; Toxicology; Library science; Chemistry; Biology; Philosophy; 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":[0.001817527,0.002100708,0.001704927,0.004961151,0.001186822,0.003176112,0.002647622,0.002259331,0.1874169],"category_scores_gemma":[0.01317276,0.0007867703,0.001919403,0.007728532,0.0004761206,0.002335265,0.001883751,0.002326313,0.21383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00217709,"about_ca_system_score_gemma":0.005241454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02645185,"about_ca_topic_score_gemma":0.04489089,"domain_scores_codex":[0.997582,0.0004446185,0.0004134088,0.0008659775,0.0004561636,0.0002378322],"domain_scores_gemma":[0.9939454,0.002026636,0.0005506226,0.001331889,0.001788366,0.0003571403],"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":[0.0001409292,0.00002306963,0.001153074,0.001212224,0.0000374884,0.00003162269,0.00002539693,0.0003122682,0.0001502959,0.00102565,0.9897322,0.006155844],"study_design_scores_gemma":[0.0001286654,0.00001641385,0.002035911,0.0004826055,0.00003724328,0.00004982818,0.00005489177,0.0002194023,0.0001943853,0.001566157,0.9951929,0.00002153364],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007184274,0.00009591075,0.0001132915,0.00006321389,0.00002557567,0.00002070235,0.9980659,0.0002039465,0.001339558],"genre_scores_gemma":[0.0002341949,0.0000970677,0.000355563,0.000105044,0.000006941553,0.00009390582,0.9980822,0.00005315905,0.000972024],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1874169,"threshold_uncertainty_score":0.626972,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01987674529056839,"score_gpt":0.3964032196181599,"score_spread":0.3765264743275915,"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."}}