{"id":"W4230732517","doi":"10.1515/iupac.88.0239","title":"Dispersing Solvent","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":"Extraction (chemistry); Computer science; Solvent extraction; Process engineering; Sample (material); Scale (ratio); Biochemical 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":[0.002161638,0.002050235,0.00169648,0.004198955,0.0009376353,0.002567661,0.002449175,0.001571093,0.06841279],"category_scores_gemma":[0.009118146,0.0006105004,0.001402418,0.007297028,0.0004149102,0.001827002,0.001934085,0.001790674,0.08169466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376202,"about_ca_system_score_gemma":0.003549594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00815348,"about_ca_topic_score_gemma":0.02063312,"domain_scores_codex":[0.9976295,0.0004174405,0.0004125896,0.0008933088,0.0004766445,0.0001705508],"domain_scores_gemma":[0.9967917,0.001177145,0.0005515165,0.0006131117,0.0007245035,0.0001421492],"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":[0.0005696903,0.00007913361,0.004953288,0.01107588,0.0002427792,0.00007048311,0.0000691151,0.0007199603,0.001970931,0.002088048,0.9305104,0.04765026],"study_design_scores_gemma":[0.000191768,0.00004211079,0.003629664,0.0006606202,0.00009221774,0.00006883401,0.00004193229,0.0002339324,0.001384683,0.001712988,0.9919153,0.00002586056],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004478439,0.0008138353,0.0007728711,0.00009733499,0.00005007968,0.00008120165,0.9950725,0.0007302603,0.001934035],"genre_scores_gemma":[0.0008521688,0.0008095808,0.002237546,0.0001235921,0.00001386924,0.0003050509,0.9942069,0.0001744454,0.001276952],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06841279,"threshold_uncertainty_score":0.2288635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03178277786363708,"score_gpt":0.4473388610826516,"score_spread":0.4155560832190145,"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."}}