{"id":"W2921541726","doi":"10.1007/s40684-019-00075-8","title":"A Topology Optimization Method for Hybrid Subtractive–Additive Remanufacturing","year":2019,"lang":"en","type":"article","venue":"International Journal of Precision Engineering and Manufacturing-Green Technology","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Department of Mechanical Engineering, University of Alberta; University of Alberta","keywords":"Remanufacturing; Subtractive color; Computer science; Mathematical optimization; Topology (electrical circuits); Offset (computer science); Upgrade; Topology optimization; Engineering drawing; Mathematics; Mechanical engineering; Engineering; Finite element method; Structural engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003134087,0.0002780094,0.0004230163,0.001277315,0.00003424125,0.00003995806,0.0004783948,0.0002544931,0.00008601767],"category_scores_gemma":[0.0001243306,0.0002843955,0.0001193064,0.00008739946,0.00003924513,0.0003582565,0.0001014188,0.0004637699,0.000005476518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240502,"about_ca_system_score_gemma":0.00001916929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004669164,"about_ca_topic_score_gemma":8.897063e-7,"domain_scores_codex":[0.9985811,0.00001664988,0.0006090207,0.0002541123,0.0002389903,0.0003001801],"domain_scores_gemma":[0.9988881,0.0003857662,0.0002109108,0.0001966196,0.0002322985,0.00008626657],"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":[0.00005783288,0.00001528703,0.00004034217,0.00003582715,0.000263218,0.00002197326,0.00006147654,0.9662131,0.003102114,0.0004607686,0.0001224763,0.02960559],"study_design_scores_gemma":[0.001504286,0.0002085444,0.0003602132,0.0001353625,0.00004773229,0.00120424,0.00008346599,0.7919861,0.1775335,0.0009426125,0.02560559,0.0003883831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1083069,0.000280915,0.8876528,0.000483406,0.002549741,0.0002484026,0.00002811894,0.0002970279,0.0001526258],"genre_scores_gemma":[0.7771569,0.0002789735,0.2220437,0.00003018456,0.0002963441,0.0000225828,0.00001367083,0.00007145611,0.00008621383],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6688499,"threshold_uncertainty_score":0.9999608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004385115049874688,"score_gpt":0.2371255474216393,"score_spread":0.2327404323717646,"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."}}