{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001088293,0.0001269854,0.0001189504,0.0001346247,0.0001061076,0.0001093784,0.0001510455,0.0000373999,0.0002270993],"category_scores_gemma":[0.00008895291,0.0001141042,0.00002651699,0.00007700893,0.000008428377,0.0006960284,0.0000413251,0.00005077141,0.0001148096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004955537,"about_ca_system_score_gemma":0.000008568298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001317478,"about_ca_topic_score_gemma":0.000009842314,"domain_scores_codex":[0.9993931,0.000003963196,0.0002034752,0.00009944912,0.00008325513,0.0002167554],"domain_scores_gemma":[0.9995545,0.00002372916,0.00002972,0.0002212749,0.0001084446,0.00006235559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004690128,0.00002547773,0.00001874964,0.0006215775,0.0001405907,4.858425e-7,0.01119222,0.5299935,0.002031271,0.0005127816,0.1472847,0.3077096],"study_design_scores_gemma":[0.0006288344,0.00004699325,0.0002838183,0.00003322005,0.0000111494,9.831833e-7,0.00009495705,0.0600429,0.8230654,0.0001358586,0.1154071,0.0002488194],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2321327,0.000009298242,0.74011,0.0003494287,0.000792541,0.001349421,0.00003310293,0.001134652,0.02408889],"genre_scores_gemma":[0.9682075,0.00001082127,0.03051236,0.0003228033,0.0003224519,0.0001341747,0.00005342946,0.00002354542,0.0004128671],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8210341,"threshold_uncertainty_score":0.4653035,"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."}}