{"id":"W3198218751","doi":"10.32920/ryerson.14655720.v1","title":"Surface roughness estimation for FDM systems","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Surface roughness; Surface finish; Robustness (evolution); Support vector machine; Computer science; Fused deposition modeling; Rapid prototyping; Algorithm; Materials science; Mechanical engineering; Machine learning; Engineering; Composite material; 3D printing","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":[0.0001321152,0.0002524622,0.0003222576,0.00005338841,0.00004863771,0.000210063,0.0002640418,0.0003835191,0.00001824208],"category_scores_gemma":[0.00009957631,0.0002521647,0.0001022261,0.00004576557,0.00002243018,0.0000510779,0.0003135333,0.0003648596,0.000011983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009298085,"about_ca_system_score_gemma":0.0000219285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005241582,"about_ca_topic_score_gemma":0.000004489218,"domain_scores_codex":[0.999126,0.0000131292,0.0002355955,0.0002985267,0.0001033343,0.0002233912],"domain_scores_gemma":[0.9992418,0.0001275771,0.00005250926,0.0004774031,0.00007631704,0.0000243519],"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":[9.421174e-7,0.000005466788,0.000009870853,0.001291357,0.00007536905,0.000002089052,0.00005117966,0.9886006,0.00007790072,0.0007559243,0.002500785,0.006628515],"study_design_scores_gemma":[0.0001071068,0.000007727347,0.0001846577,0.0004022837,0.0000344836,0.000003012656,0.0003286638,0.9404388,0.05428006,0.001395018,0.002374941,0.0004432364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2270377,0.0006465754,0.7638063,0.00005735701,0.001905055,0.0004328623,0.00005160666,0.003848474,0.002213989],"genre_scores_gemma":[0.9405684,0.00006223846,0.05833324,0.000003580833,0.00007805763,0.0001355669,0.0002286032,0.00005430702,0.0005360283],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7135307,"threshold_uncertainty_score":0.9999931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02685935120532744,"score_gpt":0.2518541447554629,"score_spread":0.2249947935501355,"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."}}