{"id":"W2127161079","doi":"10.1109/freq.1994.398259","title":"Frequency control of hydrogen masers using high accuracy calibrations","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Frequency and Time Standards","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Hydrogen maser; Frequency standard; Maser; Calibration; Series (stratigraphy); Noise (video); Computer science; Standard uncertainty; Control (management); Algorithm; Measurement uncertainty; Statistics; Mathematics; Physics; Engineering; Artificial intelligence; Electrical engineering; Optics","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":[0.002789001,0.0007987017,0.0004614026,0.001116059,0.0007143397,0.001067721,0.001062972,0.0006674121,0.001613441],"category_scores_gemma":[0.009161975,0.0004709012,0.0004004484,0.001301671,0.0006049477,0.001310321,0.001130759,0.0008757893,0.0004232488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009459759,"about_ca_system_score_gemma":0.0005458266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001608018,"about_ca_topic_score_gemma":0.001935431,"domain_scores_codex":[0.9977129,0.0004909238,0.00008330621,0.0004507327,0.001131401,0.0001307599],"domain_scores_gemma":[0.9975181,0.0008234147,0.0005256858,0.0005975605,0.0004850629,0.00005026139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005408999,0.000221577,0.01012985,0.0001376701,0.0001367042,0.00006311767,0.0003935656,0.4782747,0.1750445,0.02187038,0.001106642,0.3120804],"study_design_scores_gemma":[0.0001622987,0.0002947768,0.007888359,0.00001941651,0.00008580736,0.00008004825,0.00004371382,0.615935,0.358265,0.01206149,0.005039907,0.0001242717],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1313758,0.0001542127,0.8636174,0.0001145891,0.00002799151,0.00005396426,0.00008447266,0.002060545,0.002510916],"genre_scores_gemma":[0.6828808,0.0000803998,0.3150834,0.00003707747,0.00001767007,0.00005871605,0.0001629259,0.0005019121,0.001177041],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002789001,"threshold_uncertainty_score":0.01474983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0169812102287428,"score_gpt":0.2516985979173726,"score_spread":0.2347173876886298,"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."}}