{"id":"W4234216534","doi":"10.1515/iupac.85.0320","title":"Analyte","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 Alberta; National Research Council Canada","funders":"","keywords":"Chemical nomenclature; Terminology; Mass spectrometry; Standardization; Chemistry; Accelerator mass spectrometry; Analytical Chemistry (journal); Computer science; Environmental chemistry; Chromatography; Organic chemistry; 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":[0.002103296,0.001793495,0.001375082,0.004503862,0.001152157,0.004296288,0.00256771,0.001581996,0.1651576],"category_scores_gemma":[0.01544723,0.0006451067,0.001743967,0.007744701,0.0004105894,0.003523683,0.002853358,0.001907036,0.2301259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002168799,"about_ca_system_score_gemma":0.003926039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01765091,"about_ca_topic_score_gemma":0.02508662,"domain_scores_codex":[0.9964385,0.0004971208,0.0005172346,0.001178535,0.00107116,0.0002974492],"domain_scores_gemma":[0.9928057,0.001724395,0.0008167756,0.001605633,0.00275763,0.0002899824],"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":[0.00007739155,0.00001351642,0.001126816,0.001013376,0.00003210374,0.00001741545,0.00003135196,0.0001304196,0.0001424611,0.001344717,0.9833628,0.01270772],"study_design_scores_gemma":[0.0000361556,0.000006714796,0.001882625,0.000408659,0.00001979598,0.00003815673,0.00006315615,0.0001000483,0.0002259719,0.001731975,0.9954696,0.00001726708],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001304845,0.0002604659,0.0003808714,0.0002045049,0.00007910695,0.00002761843,0.9949033,0.0006050568,0.003408598],"genre_scores_gemma":[0.0004872951,0.0003412884,0.001238753,0.0002443173,0.00002299209,0.0001250035,0.9942002,0.0002357536,0.003104389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1651576,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01715811695107831,"score_gpt":0.4326233806983369,"score_spread":0.4154652637472586,"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."}}