{"id":"W4251275277","doi":"10.1515/iupac.88.0174","title":"Donor 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":"Extraction (chemistry); Computer science; Sample (material); Throughput; Scale (ratio); Process engineering; Sample preparation; Microwave; Chromatography; Engineering; Chemistry; 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":[0.00262101,0.00177896,0.001903334,0.002658388,0.0009884547,0.002844343,0.003036901,0.002020433,0.1228822],"category_scores_gemma":[0.01250868,0.0005677709,0.002065227,0.005370645,0.0004475319,0.00201937,0.002386875,0.002121848,0.1273511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001608079,"about_ca_system_score_gemma":0.004235268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01005006,"about_ca_topic_score_gemma":0.01722005,"domain_scores_codex":[0.9975296,0.0004763828,0.0004205495,0.0008352641,0.000431435,0.000306755],"domain_scores_gemma":[0.9952179,0.001404925,0.0008225559,0.001038595,0.001277081,0.0002390255],"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":[0.001425703,0.000100067,0.00394834,0.009593823,0.0002948001,0.00007959256,0.00005880328,0.0005025401,0.000822698,0.002470728,0.9485716,0.03213128],"study_design_scores_gemma":[0.0004174851,0.00005175926,0.00319697,0.001373155,0.0001533035,0.00008908807,0.00006329345,0.0001528222,0.0006821028,0.002125461,0.991671,0.00002379007],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003587253,0.0005625792,0.0004245378,0.0001280492,0.00006480943,0.0001324033,0.9955931,0.0002956347,0.002440121],"genre_scores_gemma":[0.00126611,0.0005999892,0.001305538,0.0003424687,0.00002914675,0.0006463475,0.9932355,0.0001163842,0.002458442],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1228822,"threshold_uncertainty_score":0.411082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03568803302657209,"score_gpt":0.4798959743056923,"score_spread":0.4442079412791202,"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."}}