{"id":"W2899068361","doi":"10.1115/detc2018-86205","title":"Information Reuse to Accelerate Customized Product Slicing for Additive Manufacturing","year":2018,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"National Science Foundation","keywords":"Slicing; Mass customization; Reuse; Computer science; Context (archaeology); Personalization; Engineering; World Wide Web","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":[0.0004409496,0.0007883094,0.0005099878,0.001222561,0.0002707051,0.0007843688,0.001076874,0.0004707889,0.002287785],"category_scores_gemma":[0.001480271,0.0004196039,0.0008222407,0.001388348,0.0005099893,0.001794979,0.001202625,0.0006014159,0.0005518771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475156,"about_ca_system_score_gemma":0.0006956934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002321092,"about_ca_topic_score_gemma":0.002681517,"domain_scores_codex":[0.9993705,0.00006391202,0.00004506637,0.0001054873,0.0003541251,0.00006104497],"domain_scores_gemma":[0.9987827,0.0002522084,0.0001284701,0.0006235876,0.0001763907,0.0000366702],"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":[0.0002904237,0.0001724377,0.002003538,0.0003883896,0.00008774859,0.0004913171,0.0006169329,0.179439,0.2545675,0.02198789,0.003806313,0.5361485],"study_design_scores_gemma":[0.00002462909,0.0001813748,0.0007798888,0.0000270559,0.00006118145,0.0003919822,0.00008236359,0.742271,0.2333829,0.01011944,0.01263245,0.00004584736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02848733,0.0003186451,0.9650321,0.0000464907,0.00002154504,0.00006102293,0.0001005964,0.003545433,0.002386959],"genre_scores_gemma":[0.3431988,0.0003208909,0.6540531,0.00004902761,0.00001528333,0.00006104886,0.0005312131,0.0004927152,0.001277869],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002321092,"threshold_uncertainty_score":0.007653356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01003066409289664,"score_gpt":0.2232997067561993,"score_spread":0.2132690426633027,"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."}}