{"id":"W4200125790","doi":"10.3390/machines10010025","title":"Data Analytics for Noise Reduction in Optical Metrology of Reflective Planar Surfaces","year":2021,"lang":"en","type":"article","venue":"Machines","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Metrology; Noise (video); Planar; Noise reduction; Computer science; Rendering (computer graphics); Analytics; Optics; Computer vision; Data mining; Computer graphics (images); Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002028804,0.00007407515,0.0001900117,0.00009811165,0.00001232851,0.000002632008,0.0001087008,0.00006769066,0.000005829268],"category_scores_gemma":[0.0001750304,0.00007175372,0.00002139472,0.0001665619,0.00002988012,0.00007797163,0.00002390023,0.00008998725,3.591956e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002537655,"about_ca_system_score_gemma":0.00001254809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006669735,"about_ca_topic_score_gemma":0.0001644201,"domain_scores_codex":[0.999498,0.00001925595,0.0001611387,0.0001474142,0.00005289097,0.0001212832],"domain_scores_gemma":[0.9996192,0.00006191123,0.00002202957,0.0002301941,0.00004963064,0.00001700774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002204849,0.0001421679,0.02223851,0.0002048051,0.00022667,0.00001431584,0.0001448922,0.03431326,0.9243733,0.003414427,0.002136489,0.01257071],"study_design_scores_gemma":[0.001961701,0.0003821601,0.02850959,0.00006161404,0.0002606255,0.00004475044,0.0002403922,0.248217,0.6833872,0.0258301,0.0105655,0.0005393851],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7646244,0.002224386,0.2284292,0.0004106773,0.0005117398,0.0002832013,0.0001762137,0.0002499826,0.003090284],"genre_scores_gemma":[0.9350765,0.0001234409,0.06456063,0.00001075775,0.0000463689,0.00001121285,0.0001189709,0.00001255962,0.00003953877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2409861,"threshold_uncertainty_score":0.2926032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06389253997757027,"score_gpt":0.3314121783850965,"score_spread":0.2675196384075262,"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."}}