{"id":"W4254526021","doi":"10.1515/iupac.88.0177","title":"Equilibration","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Analytical chemistry methods development","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Sample (material); Scale (ratio); Process engineering; Chromatography; Chemistry; Engineering; Physics","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":[0.003037389,0.002526166,0.001656312,0.003394239,0.001322176,0.003795791,0.003396014,0.001889673,0.09277395],"category_scores_gemma":[0.01626543,0.0007756201,0.002424268,0.004978038,0.0005221979,0.003431001,0.002622183,0.002411661,0.1236771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001604466,"about_ca_system_score_gemma":0.003478246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0132503,"about_ca_topic_score_gemma":0.02734325,"domain_scores_codex":[0.9960424,0.0007773254,0.0005432853,0.00163758,0.0006418573,0.0003574824],"domain_scores_gemma":[0.994535,0.00165963,0.0006045297,0.001580803,0.001394067,0.0002259805],"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":[0.0006322309,0.00007844134,0.00541292,0.004371172,0.0002437495,0.0000598556,0.00009317567,0.000957297,0.0006947633,0.003409354,0.9516694,0.03237756],"study_design_scores_gemma":[0.0002093396,0.00003787375,0.004364269,0.0006709282,0.00009813515,0.00006540921,0.00007348882,0.0005090119,0.0007254409,0.003608301,0.9896014,0.00003629415],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0006614094,0.0005837912,0.0009755501,0.0001757998,0.0001290055,0.0001151749,0.9914277,0.001659854,0.004271775],"genre_scores_gemma":[0.00146775,0.0003452929,0.001984694,0.000256513,0.00002636247,0.0003732785,0.9929302,0.0003286234,0.002287289],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09277395,"threshold_uncertainty_score":0.3103598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03285565052899398,"score_gpt":0.4533461902427365,"score_spread":0.4204905397137425,"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."}}