{"id":"W4232740297","doi":"10.1515/iupac.88.0205","title":"Speciation Analysis","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); Genetic algorithm; Process engineering; Biochemical engineering; Chemistry; Chromatography; Engineering; Biology","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":[0.00202534,0.002249381,0.001984451,0.004905778,0.001079963,0.002462613,0.002776657,0.001543832,0.06264535],"category_scores_gemma":[0.008390675,0.0005698313,0.002273641,0.007297743,0.0003628548,0.001529124,0.002217483,0.001651921,0.07173116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001637461,"about_ca_system_score_gemma":0.00365442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01376972,"about_ca_topic_score_gemma":0.02803542,"domain_scores_codex":[0.9979824,0.0003021893,0.0003019177,0.0007423963,0.000480821,0.0001902351],"domain_scores_gemma":[0.996997,0.0007754011,0.0004528525,0.0005476584,0.001106599,0.0001205559],"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":[0.0005182288,0.00006060982,0.007271694,0.01365096,0.0005451632,0.0001104362,0.00009705857,0.001327028,0.002723821,0.002613984,0.9320744,0.03900645],"study_design_scores_gemma":[0.0001390793,0.0000275844,0.004337503,0.0008340902,0.0001690999,0.00008345042,0.00005836695,0.0003441187,0.001305221,0.002085486,0.9905866,0.00002950486],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003007463,0.0003626465,0.0004905217,0.00008131739,0.00003034792,0.00004084222,0.9965023,0.0005764185,0.001615001],"genre_scores_gemma":[0.001014609,0.0004679888,0.00197587,0.0001348244,0.00001108477,0.0001690011,0.9949607,0.0001429392,0.001122977],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06264535,"threshold_uncertainty_score":0.2095696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700034140534876,"score_gpt":0.4499712831302562,"score_spread":0.4229709417249075,"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."}}