{"id":"W4239546064","doi":"10.1515/iupac.88.0168","title":"Batch Extraction","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Field-Flow Fractionation Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Computer science; Extraction (chemistry); Process engineering; Sample preparation; Sample (material); 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":[0.003316536,0.003126566,0.002459547,0.004380847,0.001170545,0.002945948,0.004112852,0.002364547,0.05393365],"category_scores_gemma":[0.01399485,0.0007853224,0.002692695,0.007110489,0.0005281342,0.002262162,0.002370272,0.002169724,0.09425878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001886574,"about_ca_system_score_gemma":0.004332555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01315651,"about_ca_topic_score_gemma":0.02284433,"domain_scores_codex":[0.9956293,0.0008063485,0.0007088683,0.001560978,0.000925607,0.000368806],"domain_scores_gemma":[0.9940202,0.001692057,0.0007389935,0.001550969,0.001786132,0.0002117139],"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":[0.0007046316,0.00009425078,0.003394517,0.008647025,0.0003701992,0.00008432012,0.00005349689,0.001128272,0.00115131,0.001647591,0.9494664,0.03325791],"study_design_scores_gemma":[0.0003094887,0.00005207106,0.004411377,0.0009614686,0.0001527333,0.00008355706,0.00005813979,0.0004200619,0.001143232,0.002294871,0.9900671,0.00004577348],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003236307,0.000479577,0.0007153391,0.00008203128,0.00006476379,0.00009606795,0.9958983,0.0007722484,0.001568155],"genre_scores_gemma":[0.000536121,0.0003164197,0.001585629,0.00009633235,0.00001485285,0.0002867039,0.9960312,0.0001075289,0.001025211],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.05393365,"threshold_uncertainty_score":0.1804261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375813727342317,"score_gpt":0.4179378968652696,"score_spread":0.4041797595918464,"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."}}