{"id":"W3028565403","doi":"10.1088/0026-1394/57/1a/09002","title":"Final report on supplementary regional comparison SIM.AUV.A-S2: calibration of pistonphone","year":2020,"lang":"en","type":"article","venue":"Metrologia","topic":"Flow Measurement and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Mutual recognition; Metrology; NIST; Calibration; Distortion (music); Mathematics; Total harmonic distortion; Statistics; Computer science; Telecommunications; Engineering; Electrical engineering; Speech recognition","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009455041,0.001148903,0.0008674081,0.002995549,0.001883955,0.002831077,0.002232235,0.00143267,0.3524833],"category_scores_gemma":[0.02473549,0.0004510741,0.0007777316,0.003125917,0.0004718042,0.00255107,0.002818757,0.00149042,0.210719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002969951,"about_ca_system_score_gemma":0.004752935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01108816,"about_ca_topic_score_gemma":0.007121199,"domain_scores_codex":[0.9891061,0.001872503,0.0007549538,0.001166098,0.00653911,0.0005612684],"domain_scores_gemma":[0.9657237,0.002518073,0.0009706176,0.002983027,0.02714472,0.0006599177],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006304985,0.0001005017,0.001908786,0.0005213351,0.00002082425,0.00007359539,0.0001793086,0.0005650674,0.003046283,0.00349006,0.9131137,0.07635012],"study_design_scores_gemma":[0.00007032128,0.0002247894,0.008971846,0.0002378222,0.00002013198,0.0001012139,0.0005238047,0.0005374597,0.006381463,0.001411904,0.9814734,0.00004583799],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01631114,0.001189435,0.0326408,0.004154837,0.01051512,0.003939709,0.3470994,0.01211565,0.5720339],"genre_scores_gemma":[0.1017031,0.001207831,0.05663146,0.002556941,0.001744679,0.00656356,0.4195424,0.01630271,0.3937474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3524833,"threshold_uncertainty_score":0.923604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07169414135554655,"score_gpt":0.2576857592620448,"score_spread":0.1859916179064982,"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."}}