{"id":"W4235677199","doi":"10.1515/iupac.88.0183","title":"Extraction Phase","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); Sample preparation; Process engineering; Throughput; Chromatography; Engineering; Chemistry; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003074436,0.002656967,0.002727228,0.003619049,0.001102787,0.003276628,0.003186281,0.002260818,0.08137792],"category_scores_gemma":[0.01179416,0.0007368013,0.002522582,0.005633074,0.0005161689,0.002458474,0.002524686,0.002664256,0.1141779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001848554,"about_ca_system_score_gemma":0.004991831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008366089,"about_ca_topic_score_gemma":0.01472329,"domain_scores_codex":[0.9969056,0.0005225788,0.0005759347,0.001099176,0.0005927051,0.0003040519],"domain_scores_gemma":[0.9947984,0.001683657,0.0008665686,0.0009772038,0.001467013,0.0002071545],"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":[0.001176691,0.00008576681,0.003276108,0.01732603,0.0003540023,0.00008624433,0.00007220334,0.0006544391,0.001826235,0.002128757,0.9384781,0.03453546],"study_design_scores_gemma":[0.0003545455,0.0000503424,0.002970853,0.001399314,0.0001593755,0.00007847994,0.00004436909,0.0001871984,0.001222868,0.001892087,0.9916077,0.0000329824],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0002494427,0.0005986484,0.000619605,0.0001006079,0.00004977049,0.0001553366,0.996194,0.0005821058,0.001450526],"genre_scores_gemma":[0.0007027987,0.0006888895,0.001955231,0.0002053673,0.00002115981,0.0007189311,0.994206,0.0001540015,0.001347659],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9186221,"threshold_uncertainty_score":0.2722363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972055519291932,"score_gpt":0.5059983851201123,"score_spread":0.466277829927193,"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."}}