{"id":"W2943103049","doi":"10.1115/1.4043672","title":"Comparing Slicing Technologies for Digital Light Processing Printing","year":2019,"lang":"en","type":"article","venue":"Journal of Computing and Information Science in Engineering","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Slicing; 3D printing; Process (computing); Digital Light Processing; Computer science; Engineering drawing; Tracing; CAD; Digital manufacturing; Digital printing; Computer graphics (images); Engineering; Manufacturing engineering; Mechanical engineering; Artificial intelligence","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":[0.0006943307,0.0001093114,0.0002010556,0.0008297244,0.00007138101,0.0003221565,0.0002728161,0.00004830013,2.469594e-7],"category_scores_gemma":[0.0004326495,0.00009865968,0.00002981538,0.0004337337,0.00004163387,0.003395519,0.0001070838,0.00026998,0.000001296845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007639494,"about_ca_system_score_gemma":0.00002292087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.643413e-7,"about_ca_topic_score_gemma":3.231702e-8,"domain_scores_codex":[0.99902,0.000001344455,0.0004718608,0.00006991345,0.0001823539,0.0002545138],"domain_scores_gemma":[0.9995202,0.00008674595,0.00016676,0.00008006799,0.0001216097,0.00002456881],"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":[0.000003808199,0.000004419211,0.01240623,0.0005019558,0.000006631913,6.587212e-7,0.00146387,0.7615598,0.002782319,0.0006737285,0.000008549329,0.220588],"study_design_scores_gemma":[0.0002579644,0.00003248343,0.008749857,0.0006536472,0.000002047677,0.00004419066,0.001651894,0.9692311,0.01799575,0.00006951508,0.001160549,0.0001510085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8735256,0.00006034549,0.1251398,0.00004152279,0.0002650548,0.00008243189,2.87202e-7,0.0003216559,0.0005632749],"genre_scores_gemma":[0.989121,0.0000135578,0.01083073,0.000003521184,0.00002226142,8.979636e-7,2.644487e-7,0.000006802572,9.137816e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.220437,"threshold_uncertainty_score":0.4023225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007500004313058949,"score_gpt":0.2177717588945945,"score_spread":0.2102717545815356,"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."}}