{"id":"W3084688822","doi":"10.1109/cpem49742.2020.9191778","title":"Abbe Offset Measurement in the NRC Kibble Balance","year":2020,"lang":"en","type":"article","venue":"","topic":"Scientific Measurement and Uncertainty Evaluation","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Offset (computer science); Measurement uncertainty; Realization (probability); Standardization; Balance (ability); Observational error; Computer science; Electronic engineering; Algorithm; Engineering; Statistics; Mathematics; Programming language; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.02718734,0.00009317013,0.0001463299,0.00008449993,0.0001185019,0.000352726,0.001067655,0.00003091432,0.002646632],"category_scores_gemma":[0.009097922,0.00004792319,0.00006844365,0.001340615,0.0000438287,0.0002233356,0.00004937943,0.0001039004,0.001558022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007257197,"about_ca_system_score_gemma":0.0001428456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004228685,"about_ca_topic_score_gemma":0.0004367763,"domain_scores_codex":[0.9896789,0.0004737358,0.0005326206,0.0004572948,0.008637231,0.0002202492],"domain_scores_gemma":[0.9982199,0.0002909833,0.00011336,0.0004595676,0.0008374819,0.00007871525],"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":[0.00004734094,0.00007277748,0.02358875,0.000002891896,0.000005886606,0.00000282043,0.004101473,0.0006537652,0.00538238,0.004095185,0.9354917,0.02655505],"study_design_scores_gemma":[0.001797271,0.0001716063,0.08715852,0.00002449138,0.00001847884,0.000002377338,0.009882107,0.1698368,0.002955519,0.03205835,0.695706,0.0003885399],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2161027,0.0005656036,0.04007455,0.2524257,0.002434317,0.002109435,0.00001247493,0.0001588876,0.4861163],"genre_scores_gemma":[0.9933622,0.000002107777,0.0002556972,0.005511344,0.00009598664,0.00001700776,0.000001844317,0.000002888259,0.0007509123],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7772595,"threshold_uncertainty_score":0.9992489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6748938234175993,"score_gpt":0.432366560292906,"score_spread":0.2425272631246933,"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."}}