{"id":"W4245689273","doi":"10.1515/iupac.88.0184","title":"Extraction Rate","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); Process engineering; Sample (material); 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":[0.00294924,0.002311457,0.002323296,0.005001435,0.0007651297,0.002933048,0.002708265,0.001640745,0.06432821],"category_scores_gemma":[0.01900765,0.0006109154,0.002614476,0.007355607,0.0004034254,0.002412532,0.001816156,0.002240212,0.09605099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001656304,"about_ca_system_score_gemma":0.002442535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0124129,"about_ca_topic_score_gemma":0.0140838,"domain_scores_codex":[0.9959395,0.0005993951,0.0008700574,0.001478065,0.0008381477,0.0002748602],"domain_scores_gemma":[0.9928895,0.002313111,0.001032083,0.001345094,0.002209835,0.0002103029],"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":[0.0007633677,0.0000743601,0.00894531,0.007857003,0.0004163055,0.00005666062,0.00008316983,0.001094531,0.001039081,0.002026933,0.9389553,0.03868806],"study_design_scores_gemma":[0.0003380535,0.00004511676,0.01126057,0.001209023,0.0001875897,0.0001085201,0.00007570862,0.0003548184,0.0009606592,0.002105744,0.983293,0.00006125752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003907713,0.0004505662,0.0002712705,0.00007590609,0.00004952874,0.00005335534,0.9969954,0.0003361028,0.00137707],"genre_scores_gemma":[0.001305137,0.0005384628,0.001108317,0.0001518429,0.00002323952,0.0003136599,0.9947898,0.0001646208,0.001604879],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06432821,"threshold_uncertainty_score":0.2151993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0335884587246072,"score_gpt":0.4690742245285988,"score_spread":0.4354857658039916,"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."}}