{"id":"W4405337584","doi":"10.1007/s00170-024-14903-y","title":"Edge adjacency graph and neural network architecture for machining feature recognition","year":2024,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Mitacs","keywords":"Artificial neural network; Adjacency list; Machining; Artificial intelligence; Computer science; Graph; Feature (linguistics); Deep learning; Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Architecture; Theoretical computer science; Engineering; Algorithm","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":[0.0001554093,0.0001481676,0.0001478219,0.0003183077,0.00007310751,0.0000911095,0.0003560927,0.0001066616,0.00001153742],"category_scores_gemma":[0.00003838017,0.0001058336,0.00007411372,0.00008510982,0.00004769102,0.0001903288,0.00005605112,0.0005218209,7.607306e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004389823,"about_ca_system_score_gemma":0.00001090378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.772033e-7,"about_ca_topic_score_gemma":0.000005574998,"domain_scores_codex":[0.9993087,0.000007945439,0.0002311586,0.0001259731,0.0001449291,0.0001812815],"domain_scores_gemma":[0.9995971,0.0001222645,0.00008877783,0.00008843066,0.00007410289,0.00002937865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00006505572,0.000004112246,0.00001012916,0.00007559163,0.0001357397,0.00001967278,0.0001123649,0.5227227,0.0008752852,0.0003286511,0.0007876558,0.474863],"study_design_scores_gemma":[0.001908017,0.0004437163,0.0006969686,0.001534747,0.000221413,0.002959077,0.0003105834,0.07661359,0.3221929,0.4874547,0.1048518,0.0008124768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7448704,0.01105649,0.2287421,0.009433187,0.004742309,0.000303152,0.00002808412,0.0005946856,0.0002296826],"genre_scores_gemma":[0.9724779,0.001358731,0.02535821,0.00009844873,0.0005920851,0.0000167037,0.00001129283,0.00003884987,0.00004781294],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4871261,"threshold_uncertainty_score":0.4315769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007467383236330997,"score_gpt":0.2290286501652424,"score_spread":0.2215612669289114,"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."}}