{"id":"W3194430280","doi":"10.1364/cleo_at.2021.ath4p.3","title":"Applicability of artificial neural network for modeling and prediction of the laser polished surface quality","year":2021,"lang":"en","type":"article","venue":"Conference on Lasers and Electro-Optics","topic":"Surface Roughness and Optical Measurements","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Waviness; Artificial neural network; Polishing; Surface roughness; Surface (topology); Process (computing); Surface finish; Laser; Materials science; Computer science; Wetting; Surface finishing; Quality (philosophy); Artificial intelligence; Mechanical engineering; Optics; Engineering; Mathematics; Composite material; Geometry; 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":[0.0002291573,0.0001062133,0.0002188181,0.000007186146,0.00006075732,0.00002159007,0.00005456419,0.0000755689,0.000003677998],"category_scores_gemma":[0.00006425986,0.00008624403,0.00004536309,0.0001019898,0.00005896881,0.00004222699,0.00002104068,0.0001176712,7.933195e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001494597,"about_ca_system_score_gemma":0.00003322784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001146079,"about_ca_topic_score_gemma":0.00002773678,"domain_scores_codex":[0.9992009,0.00003675376,0.0002656818,0.0001585618,0.0001340943,0.000203958],"domain_scores_gemma":[0.9994879,0.00008852465,0.00004015173,0.0001748364,0.0001569637,0.00005159429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002413601,0.0001295545,0.008168851,0.0005291266,0.0001056717,3.103521e-7,0.0001844161,0.856684,0.1041533,0.02345455,0.00006365928,0.006285212],"study_design_scores_gemma":[0.0002456731,0.00007025531,0.001613367,0.00003493892,0.00003458479,3.728509e-7,0.0001167139,0.9498303,0.04384934,0.00411123,0.00001144699,0.000081781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895357,0.0001095576,0.009641296,0.0001291385,0.0001239771,0.0001818349,0.00005353535,0.0000200699,0.0002048993],"genre_scores_gemma":[0.9988089,0.0000981178,0.001005917,0.0000188027,0.00003753716,0.000006752342,0.00000720665,0.000009895072,0.000006920821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09314628,"threshold_uncertainty_score":0.3516929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0511671050162939,"score_gpt":0.2605908061902127,"score_spread":0.2094237011739188,"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."}}