{"id":"W4242201776","doi":"10.32920/ryerson.14652987.v1","title":"Predictive modeling of surface finish in fine grinding","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Grinding; Materials science; Surface roughness; Ellipsoid; Enhanced Data Rates for GSM Evolution; Surface (topology); Surface finish; Process (computing); Mechanical engineering; Chip; Consistency (knowledge bases); Engineering drawing; Metallurgy; Computer science; Composite material; Geometry; Engineering; Mathematics; Physics; Artificial intelligence","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":[0.0004401974,0.0004291261,0.0007302068,0.0003553318,0.0002746853,0.0008527052,0.000821854,0.0008045462,0.001195382],"category_scores_gemma":[0.001142083,0.0005060403,0.0006815562,0.0003335092,0.0006066773,0.0005088722,0.0003872907,0.0006769125,0.000206055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008432174,"about_ca_system_score_gemma":0.0006945474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01758629,"about_ca_topic_score_gemma":0.008880815,"domain_scores_codex":[0.9997999,0.00002978119,0.000006534271,0.00004655544,0.00007995965,0.00003721662],"domain_scores_gemma":[0.9995678,0.0002568739,0.00005680777,0.00003222055,0.00006895027,0.00001727476],"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":[0.000004327863,0.000004206041,0.0001227829,0.000003827489,0.000001661347,0.00000725396,0.000004003436,0.9984301,0.000361927,0.0002869261,0.00002209793,0.0007509461],"study_design_scores_gemma":[6.539187e-7,0.000002618033,0.00006776962,3.928183e-7,4.357001e-7,8.843133e-7,7.156499e-7,0.9997151,0.00007340156,0.0001102028,0.00002701199,7.829726e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.415933,0.0005326641,0.572231,0.0002550351,0.00006140658,0.0001003897,0.0003819662,0.0007433333,0.009761273],"genre_scores_gemma":[0.9859658,0.0001959309,0.01052079,0.00001576763,0.000008732576,0.00005782103,0.0001460337,0.00004835064,0.003040851],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01758629,"threshold_uncertainty_score":0.03496784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01384643837900599,"score_gpt":0.2371927901630209,"score_spread":0.2233463517840149,"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."}}