{"id":"W2968036747","doi":"10.1108/rpj-01-2019-0007","title":"Additive manufacturing infill optimization for automotive 3D-printed ABS components","year":2019,"lang":"en","type":"article","venue":"Rapid Prototyping Journal","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Infill; Automotive industry; Ultimate tensile strength; Structural engineering; Acrylonitrile butadiene styrene; Materials science; Stress (linguistics); Composite material; Engineering drawing; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003059107,0.0002828304,0.0003012845,0.0002889728,0.0002238824,0.0001288878,0.0003199227,0.0001637767,0.0005818181],"category_scores_gemma":[0.0001106791,0.0002699379,0.0001389573,0.00008575091,0.00004672554,0.0002871102,0.00007982329,0.0006399216,0.00007706042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001802727,"about_ca_system_score_gemma":0.00001833785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000181163,"about_ca_topic_score_gemma":2.571082e-7,"domain_scores_codex":[0.9986842,0.00003253818,0.0003767846,0.0002481651,0.0001857594,0.0004725076],"domain_scores_gemma":[0.9992123,0.0001572655,0.0001641587,0.0002319369,0.0001515465,0.00008274546],"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":[0.0002347231,0.0000762371,0.0002514719,0.0005073901,0.0006935435,0.00001922249,0.0008187382,0.5313247,0.001714626,0.0003030845,0.001859125,0.4621971],"study_design_scores_gemma":[0.002392274,0.0003536794,0.005284632,0.0009975915,0.0000524607,0.0001706724,0.0003418988,0.4361295,0.5004304,0.002353729,0.05053738,0.0009558236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4833906,0.0002931472,0.5031771,0.0001916496,0.001773046,0.003908482,0.00009226593,0.002644505,0.004529194],"genre_scores_gemma":[0.937972,0.0002342395,0.06094861,0.00005404007,0.0002607518,0.0002410669,0.0000505274,0.00009920642,0.0001395562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4987158,"threshold_uncertainty_score":0.9999753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01395349194289356,"score_gpt":0.2234791445390152,"score_spread":0.2095256525961217,"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."}}