{"id":"W1492251240","doi":"10.1002/sca.21072","title":"Roughness parameter selection for novel manufacturing processes","year":2013,"lang":"en","type":"article","venue":"Scanning","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Selection (genetic algorithm); Surface roughness; Surface finish; Manufacturing process; Single point; Computer science; Process (computing); Work (physics); Point (geometry); Materials science; Process variable; Manufacturing engineering; Mechanical engineering; Mathematics; Process engineering; Biological system; Artificial intelligence; Engineering; Composite material; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00006620247,0.0001014112,0.00009712844,0.00007876845,0.0000642527,0.00006860957,0.00006976589,0.00005263102,0.00004871214],"category_scores_gemma":[0.00007034997,0.00009970429,0.00002837543,0.00009767018,0.000007649703,0.000406885,0.00000951432,0.00006691901,0.0000174531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004129764,"about_ca_system_score_gemma":0.000007707385,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002364259,"about_ca_topic_score_gemma":0.000003330263,"domain_scores_codex":[0.9994785,0.000002396954,0.000144363,0.0001118806,0.00007541028,0.0001874953],"domain_scores_gemma":[0.9997319,0.00007238679,0.00002552908,0.00007496509,0.00006377611,0.00003142854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000321633,0.0001002739,0.007158,0.005214156,0.000310022,6.863875e-7,0.002668363,0.4029117,0.3079076,0.002148066,0.01260748,0.2589415],"study_design_scores_gemma":[0.0001541544,0.00002112523,0.001102814,0.00006722179,0.00000915144,0.000003638997,0.00002592631,0.1159794,0.8746642,0.003060109,0.004708407,0.0002038477],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7114497,0.00004081576,0.2863354,0.00001423412,0.0001366657,0.0003516652,0.000001640979,0.0008534946,0.0008164074],"genre_scores_gemma":[0.951041,0.000001668578,0.04827638,0.00002323615,0.00008042189,0.000243292,0.000006024684,0.00003506125,0.0002928519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5667566,"threshold_uncertainty_score":0.4065823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01943325481394165,"score_gpt":0.251632747191375,"score_spread":0.2321994923774333,"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."}}