{"id":"W4400673654","doi":"10.2139/ssrn.4896959","title":"Increasing 3d Printing Accuracy Through Convolutional Neural Network-Based Compensation for Geometric Deviations","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Convolutional neural network; Compensation (psychology); Computer science; 3D printing; Artificial intelligence; Artificial neural network; Computer vision; Pattern recognition (psychology); Engineering; Psychology; Mechanical 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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001434103,0.0003681731,0.0003435811,0.0003522676,0.0003812222,0.0003887422,0.0003002004,0.0002744287,0.00002673917],"category_scores_gemma":[0.00027457,0.0003828297,0.0002375673,0.0003863181,0.00002400508,0.000203548,0.0001367104,0.003352896,0.000009054808],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001708727,"about_ca_system_score_gemma":0.001554244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004591606,"about_ca_topic_score_gemma":0.0000829975,"domain_scores_codex":[0.9969506,0.00006632285,0.0006263216,0.0003395309,0.0003168975,0.001700319],"domain_scores_gemma":[0.9988426,0.0003767412,0.000297436,0.0001874094,0.0002321033,0.00006369451],"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":[0.00001784663,0.00001350981,0.0003150402,0.0003912155,0.0002146441,6.714183e-7,0.00004422963,0.9769194,0.000007277795,0.01168968,0.00009099476,0.01029551],"study_design_scores_gemma":[0.0004233865,0.00003505416,0.0005683642,0.000260691,0.0001922805,0.00007592718,0.00002969093,0.861708,0.00005989887,0.135246,0.001005882,0.0003948456],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09238151,0.009332789,0.8951035,0.0002912505,0.001639715,0.0005522308,0.00002500343,0.0003836234,0.0002904215],"genre_scores_gemma":[0.9800245,0.001444463,0.01632747,0.00005266585,0.001591826,0.00007948803,0.0003097355,0.0001095133,0.00006033258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.887643,"threshold_uncertainty_score":0.9998624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0165712502170276,"score_gpt":0.2525149851173836,"score_spread":0.235943734900356,"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."}}