{"id":"W4237305737","doi":"10.1515/iupac.88.0166","title":"Acceptor 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); Microwave; Sample (material); Throughput; Process engineering; Scale (ratio); Sample preparation; Chromatography; Chemistry; Engineering; Physics; Telecommunications","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":[0.002183215,0.002242012,0.001904664,0.003459933,0.001000428,0.003028869,0.003013793,0.002152419,0.09477706],"category_scores_gemma":[0.01024288,0.0006046748,0.002341581,0.005896363,0.0004490095,0.002116881,0.002296614,0.002100307,0.1142957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001540963,"about_ca_system_score_gemma":0.003873509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009929755,"about_ca_topic_score_gemma":0.01944701,"domain_scores_codex":[0.9975697,0.0004020903,0.0003726734,0.000906376,0.0004637521,0.0002852583],"domain_scores_gemma":[0.9962875,0.001129419,0.0006360985,0.0008227868,0.0009522932,0.0001719901],"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":[0.00123425,0.0001206193,0.005354865,0.01162654,0.0003830191,0.0000972607,0.00006378895,0.0009317831,0.001493944,0.003192989,0.9357719,0.03972907],"study_design_scores_gemma":[0.0003026326,0.00005488612,0.003417733,0.001058254,0.0001704804,0.00008925684,0.00005274603,0.0002831965,0.0009632731,0.002482223,0.9910994,0.00002605327],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003659317,0.0006112505,0.0004809181,0.00009591998,0.00005360346,0.00009206439,0.9959293,0.0004146811,0.001956237],"genre_scores_gemma":[0.000993127,0.0005298548,0.001278183,0.0001842928,0.00001899776,0.0003300001,0.9950777,0.00009811612,0.001489698],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09477706,"threshold_uncertainty_score":0.3170609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03839869239838337,"score_gpt":0.4932810712436859,"score_spread":0.4548823788453025,"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."}}