{"id":"W4225248428","doi":"10.3390/machines10050331","title":"Modeling and Predicting the Machined Surface Roughness and Milling Power in Scot’s Pine Helical Milling Process","year":2022,"lang":"en","type":"article","venue":"Machines","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Rotational speed; Rotation (mathematics); Machinability; Machining; Materials science; Surface roughness; Power (physics); Mechanical engineering; Response surface methodology; Surface finish; Process (computing); Engineering drawing; Composite material; Geometry; Metallurgy; Mathematics; Engineering; Computer science; Statistics; 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":[0.0003111525,0.0001954522,0.0001948189,0.00006769213,0.0004091676,0.00006551785,0.0001365643,0.00003790869,0.00001278111],"category_scores_gemma":[0.00005894366,0.0001605318,0.00001934364,0.0002936313,0.00002616791,0.0001910146,0.0001305489,0.0004202203,2.127192e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003029085,"about_ca_system_score_gemma":0.00001115718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009616314,"about_ca_topic_score_gemma":0.00005486636,"domain_scores_codex":[0.9989968,0.00003706526,0.0002675836,0.0002681476,0.0001759078,0.0002544799],"domain_scores_gemma":[0.9996947,0.00007972727,0.00003431111,0.0001176085,0.00002677714,0.0000468924],"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":[0.00001967857,0.00001051024,0.008901365,0.0001264518,0.000009078059,0.00000312589,0.002254347,0.9866816,0.0003387879,0.0000492914,0.000001255376,0.001604528],"study_design_scores_gemma":[0.0003759569,0.0000284837,0.0002560331,0.00004729849,0.00001079661,0.00002229772,0.0006266844,0.9976943,0.00005147504,0.0006542644,0.00004041077,0.0001920426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.895961,0.005758862,0.09740331,0.0002008028,0.0001747272,0.0001891381,0.00001306783,0.0001878437,0.0001111799],"genre_scores_gemma":[0.9963991,0.0002818215,0.003078486,0.00007834788,0.00004628692,0.00003144057,0.0000169029,0.00005281815,0.00001479362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.100438,"threshold_uncertainty_score":0.6546296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00708393471346402,"score_gpt":0.2303512092597267,"score_spread":0.2232672745462627,"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."}}