{"id":"W4250787890","doi":"10.1515/iupac.88.0155","title":"Boundary Layer Thickness","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; Scale (ratio); Layer (electronics); Process engineering; Nanotechnology; Materials science; 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":[0.001146263,0.001976368,0.001425692,0.003522895,0.0007442686,0.002525146,0.00284919,0.00156087,0.04212818],"category_scores_gemma":[0.006034904,0.000563424,0.001778078,0.00498593,0.0003175802,0.001922067,0.001552422,0.001791139,0.05686352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00123631,"about_ca_system_score_gemma":0.001719216,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01856732,"about_ca_topic_score_gemma":0.03481435,"domain_scores_codex":[0.9988133,0.000145918,0.0001738103,0.0004908806,0.0002458239,0.0001302406],"domain_scores_gemma":[0.9978963,0.0005772359,0.0003455898,0.0004493469,0.0006339026,0.00009757527],"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":[0.0004242727,0.00006197863,0.01206279,0.004422144,0.0002809215,0.00007513072,0.00007039822,0.00166338,0.001089867,0.002121542,0.950762,0.02696568],"study_design_scores_gemma":[0.0002439402,0.00003334129,0.01832348,0.001024116,0.0001413463,0.0001227783,0.000144923,0.001338419,0.001454843,0.0042613,0.9728559,0.00005547956],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005195749,0.000341515,0.0003034205,0.00006489662,0.00003800626,0.00001886558,0.996963,0.0004412911,0.001309488],"genre_scores_gemma":[0.002011192,0.0003681031,0.001324401,0.00007956454,0.0000155156,0.0001242742,0.9948909,0.0001173723,0.0010686],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.04212818,"threshold_uncertainty_score":0.1409328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03568260671614117,"score_gpt":0.4463405609489829,"score_spread":0.4106579542328417,"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."}}