{"id":"W4236669562","doi":"10.1515/iupac.88.0371","title":"Boundary Layer Model","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; Sample (material); Extraction (chemistry); Perspective (graphical); Sample preparation; Scale (ratio); Matrix (chemical analysis); Mass transfer; Data mining; Process engineering; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.001393348,0.001428842,0.0009831917,0.001562085,0.0005600597,0.002411179,0.003824373,0.002120318,0.04530779],"category_scores_gemma":[0.008149586,0.0004550716,0.002050779,0.002180481,0.000331495,0.001817551,0.00107049,0.001958301,0.03543315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001561222,"about_ca_system_score_gemma":0.001903025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03089554,"about_ca_topic_score_gemma":0.03655586,"domain_scores_codex":[0.9991282,0.0001986772,0.00007926791,0.0003370843,0.0001458533,0.0001108856],"domain_scores_gemma":[0.998334,0.0008588752,0.00009970892,0.0002878514,0.0003611347,0.000058531],"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":[0.0005318571,0.0001920483,0.007538261,0.001615557,0.0003167262,0.0001926584,0.0001068897,0.1268323,0.0005196999,0.02100752,0.7661012,0.07504532],"study_design_scores_gemma":[0.0004550173,0.00008219518,0.003646963,0.0006282264,0.0001374924,0.0001873014,0.0002047051,0.3539773,0.001089606,0.05323229,0.586292,0.00006698717],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006743146,0.00210221,0.03612559,0.001815097,0.0004343262,0.0003281798,0.9311326,0.008102821,0.01321609],"genre_scores_gemma":[0.03947055,0.001312125,0.04220594,0.0007325146,0.0001021684,0.0008526846,0.903818,0.000769212,0.01073691],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04530779,"threshold_uncertainty_score":0.1515697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04413690282468955,"score_gpt":0.4546475429672682,"score_spread":0.4105106401425787,"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."}}