{"id":"W6981062255","doi":"","title":"Development of a Scalable Machining Feature Recognition System","year":2023,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Overfitting; CAD; Feature (linguistics); Pattern recognition (psychology); Crossover; Dropout (neural networks); Feature recognition; Scalability; Machining","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":[0.0004279416,0.0007646825,0.0009892541,0.0007278853,0.0003805798,0.0009597492,0.002511772,0.0006240817,0.008807703],"category_scores_gemma":[0.001018739,0.0005589824,0.0007027435,0.0008600906,0.0001944887,0.001870287,0.001179093,0.001081186,0.00823097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000555753,"about_ca_system_score_gemma":0.001025957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005293532,"about_ca_topic_score_gemma":0.005732347,"domain_scores_codex":[0.9993568,0.00002304431,0.00004248347,0.0002277719,0.0002790323,0.00007086161],"domain_scores_gemma":[0.9995426,0.00004986674,0.00002078252,0.0001400239,0.0002197085,0.00002714651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003517748,0.0002988752,0.001717586,0.0003153375,0.0001052785,0.0003428284,0.00009038456,0.02853725,0.1083543,0.003987047,0.04372913,0.8121702],"study_design_scores_gemma":[0.00009981838,0.0003555716,0.003119624,0.00004711086,0.00007891857,0.0003227974,0.00009869008,0.8217198,0.1056284,0.003731631,0.06472353,0.00007413289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03783304,0.0004938138,0.8558985,0.0002457869,0.0003703929,0.0007453412,0.00388598,0.08928096,0.01124622],"genre_scores_gemma":[0.2325218,0.0004147945,0.7247462,0.000343327,0.0001210418,0.0008673462,0.02186033,0.001114645,0.01801052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008807703,"threshold_uncertainty_score":0.02946466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03972539804854933,"score_gpt":0.219584445052373,"score_spread":0.1798590470038236,"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."}}