{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001838419,0.0005707505,0.0004799378,0.002974608,0.0004183442,0.001757897,0.0007630803,0.0006821713,0.0031228],"category_scores_gemma":[0.007077384,0.000285288,0.0006262067,0.003251162,0.0007365352,0.002452723,0.0009325582,0.0005373968,0.0005521236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001118454,"about_ca_system_score_gemma":0.001044693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003219618,"about_ca_topic_score_gemma":0.002214955,"domain_scores_codex":[0.9967486,0.0004205233,0.0003274286,0.000253936,0.002016972,0.0002325622],"domain_scores_gemma":[0.9937972,0.002867955,0.0005593391,0.000827589,0.001802905,0.0001450784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002473105,0.0001830454,0.005475696,0.002275805,0.0001618575,0.0003121849,0.0007429945,0.06786016,0.09658495,0.02560301,0.004798995,0.7935281],"study_design_scores_gemma":[0.0002979363,0.005726535,0.02812368,0.0009368616,0.0007524743,0.002792011,0.001825212,0.2976224,0.5164793,0.02273046,0.1221689,0.0005441303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3923824,0.04638216,0.501267,0.0007593729,0.0008985927,0.0007589769,0.002030043,0.004171394,0.05135001],"genre_scores_gemma":[0.6093856,0.01572642,0.3683965,0.0002001723,0.00009365763,0.0002413326,0.001888253,0.0007150546,0.00335298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003219618,"threshold_uncertainty_score":0.01044685,"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."}}