{"id":"W4254583491","doi":"10.1515/iupac.88.0189","title":"Fractionation (of Analytes)","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); Analyte; Fractionation; Sample (material); Process engineering; Sample preparation; Scale (ratio); Throughput; Chromatography; Chemistry; Engineering","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":[0.002422497,0.003070831,0.002445691,0.005146868,0.001101708,0.003422133,0.002619716,0.002035351,0.07072823],"category_scores_gemma":[0.01252464,0.0007114937,0.002789028,0.006059207,0.0004981129,0.002173815,0.002990638,0.00199078,0.09652864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001636349,"about_ca_system_score_gemma":0.003957239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01011033,"about_ca_topic_score_gemma":0.0183537,"domain_scores_codex":[0.9962518,0.0005397356,0.0006767608,0.001521688,0.0006607183,0.0003494083],"domain_scores_gemma":[0.9949924,0.001431282,0.0008239085,0.001339193,0.001196879,0.0002163622],"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":[0.0007756749,0.0000703717,0.006947316,0.01507981,0.0004376737,0.00008487541,0.00008384611,0.0006497419,0.00237217,0.001975227,0.9338968,0.03762652],"study_design_scores_gemma":[0.0002781205,0.00004706066,0.00603431,0.001434598,0.0001492319,0.0001079945,0.00006672485,0.0002096089,0.001431631,0.00189702,0.9883054,0.00003831744],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002263971,0.0004714382,0.0002745117,0.00006143763,0.00004525023,0.00004750245,0.9974711,0.0004109828,0.0009913697],"genre_scores_gemma":[0.0007238808,0.0004196966,0.001311137,0.0001462104,0.00001690485,0.0001968058,0.9962912,0.0001023224,0.000791889],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07072823,"threshold_uncertainty_score":0.2366095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02933833875090861,"score_gpt":0.4614449243192905,"score_spread":0.4321065855683819,"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."}}