{"id":"W4233058960","doi":"10.1515/iupac.88.0316","title":"Electrodialysis","year":2017,"lang":"en","type":"dataset","venue":"IUPAC Standards Online","topic":"Housing, Finance, and Neoliberalism","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; National Research Council Canada","funders":"","keywords":"Electrodialysis; Computer science; Extraction (chemistry); Process engineering; Sample (material); Scale (ratio); Biochemical engineering; Sample preparation; Chromatography; Membrane; 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":[0.00236291,0.001853402,0.001778629,0.001898984,0.000716795,0.002557486,0.002659629,0.001800817,0.0214252],"category_scores_gemma":[0.007429868,0.0004617947,0.001510746,0.003408403,0.0004327676,0.001241941,0.001830494,0.002020571,0.03713293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001361928,"about_ca_system_score_gemma":0.002392358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007642998,"about_ca_topic_score_gemma":0.01336267,"domain_scores_codex":[0.9978732,0.000433263,0.0002697681,0.000763754,0.0004886138,0.0001713709],"domain_scores_gemma":[0.9972284,0.0008073178,0.0004994238,0.0006633897,0.0006759638,0.0001254571],"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":[0.0008238557,0.0001831636,0.009705172,0.005290016,0.0004640583,0.00008864843,0.00004628399,0.001340598,0.002012838,0.002121916,0.9369993,0.04092421],"study_design_scores_gemma":[0.0004506306,0.0001575803,0.01851482,0.0009130103,0.0002724603,0.0003174999,0.00008534699,0.001687773,0.004039986,0.004821789,0.9686517,0.00008732854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001860192,0.001945809,0.002544112,0.0003229443,0.0001942018,0.0001391723,0.9883282,0.001405887,0.003259517],"genre_scores_gemma":[0.004204465,0.001610855,0.004005107,0.000499324,0.00007310378,0.0007105189,0.9862816,0.000202646,0.002412332],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0214252,"threshold_uncertainty_score":0.07167441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382001247422449,"score_gpt":0.3460919429609344,"score_spread":0.3222719304867099,"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."}}