{"id":"W2547344027","doi":"10.1109/ccece.2016.7726783","title":"Machine learning for quality prediction in abrasion-resistant material manufacturing process","year":2016,"lang":"en","type":"article","venue":"","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Process (computing); Computer science; Abrasion (mechanical); Quality (philosophy); Support vector machine; Product (mathematics); Machine learning; Manufacturing engineering; Process engineering; Artificial intelligence; Engineering; Mechanical engineering; Mathematics","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":[0.0009864151,0.0005894821,0.000762336,0.0007148777,0.0002721258,0.0005743091,0.0006961498,0.0007148532,0.0005071962],"category_scores_gemma":[0.003529176,0.0002679844,0.0005294114,0.0006616492,0.0003283923,0.0006116722,0.0003627202,0.0008907366,0.0001715207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005492514,"about_ca_system_score_gemma":0.0005460318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005081267,"about_ca_topic_score_gemma":0.002251035,"domain_scores_codex":[0.9995403,0.0001156908,0.00003452783,0.0001023914,0.0001614311,0.00004564726],"domain_scores_gemma":[0.9987692,0.0007053279,0.0001605692,0.0000867073,0.000250844,0.00002728668],"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":[0.0001635341,0.0001477589,0.004747334,0.00009585886,0.00004097657,0.0000753745,0.00004381821,0.8655075,0.005958493,0.001102688,0.0004806644,0.1216359],"study_design_scores_gemma":[0.000001267789,0.00001256143,0.0002695151,0.000001119966,0.000001221798,0.000003333728,0.000001495253,0.9988386,0.0006103332,0.0002293829,0.00002977387,0.000001350135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2606066,0.0009035987,0.736171,0.0002103834,0.00004352584,0.00005767568,0.0001378786,0.001091595,0.0007777482],"genre_scores_gemma":[0.9565009,0.0001758744,0.04270279,0.00002294833,0.00001431188,0.00004408251,0.000164378,0.00002117257,0.0003535251],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005081267,"threshold_uncertainty_score":0.0101034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01235957390671405,"score_gpt":0.2651435432257859,"score_spread":0.2527839693190718,"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."}}