{"id":"W3082362945","doi":"10.1088/0026-1394/57/1a/04002","title":"Calibration of 1-D CMM artefacts: step gauges (EURAMET.L-K5.2016)","year":2020,"lang":"en","type":"article","venue":"Metrologia","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mutual recognition; Mathematics; Calibration; Statistics; Equivalence (formal languages); Metrology; Discrete mathematics","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":[0.0150587,0.00110598,0.0005348695,0.002322411,0.0008896248,0.001728035,0.00214171,0.001749405,0.01038305],"category_scores_gemma":[0.02093884,0.0005215774,0.0007304595,0.002223978,0.0008354688,0.001164396,0.002883926,0.001041688,0.006057749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001369,"about_ca_system_score_gemma":0.001067678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002798792,"about_ca_topic_score_gemma":0.004723193,"domain_scores_codex":[0.9821264,0.003361176,0.0008226536,0.002604344,0.01069897,0.0003862884],"domain_scores_gemma":[0.986678,0.002163769,0.001610859,0.00315919,0.006071776,0.0003165455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004225199,0.0007147296,0.1195768,0.002235342,0.0003351764,0.0004724921,0.002426921,0.009064147,0.1838041,0.006735886,0.05070346,0.6197057],"study_design_scores_gemma":[0.0003352134,0.003220832,0.4734489,0.0006498201,0.0002852964,0.003196455,0.001511488,0.02213852,0.256965,0.003461504,0.2344106,0.0003762517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4803798,0.003445488,0.4042128,0.0008821508,0.002421306,0.002978541,0.02433679,0.007392561,0.07395063],"genre_scores_gemma":[0.7078558,0.0005888153,0.2601078,0.0005486399,0.0001654024,0.001110929,0.01253661,0.00148057,0.01560543],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0150587,"threshold_uncertainty_score":0.07963896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454091074039185,"score_gpt":0.2105292859913113,"score_spread":0.1959883752509195,"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."}}