{"id":"W4411017395","doi":"10.1016/j.rcim.2025.103059","title":"Novel calibration method for robotic bottom-up vat polymerization additive manufacturing systems","year":2025,"lang":"en","type":"article","venue":"Robotics and Computer-Integrated Manufacturing","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Mitacs; Nissan North America","keywords":"Calibration; Computer science; Polymerization; Process engineering; Manufacturing engineering; Materials science; Engineering; Mathematics; Composite material; Statistics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001960975,0.0005490567,0.0005743379,0.0004652203,0.0003475157,0.0004081111,0.0003194774,0.0003273205,0.000005682602],"category_scores_gemma":[0.00003029527,0.0005275633,0.000126254,0.0001618551,0.00006509442,0.0002486486,0.0001752575,0.0005111096,0.000002606191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001599912,"about_ca_system_score_gemma":0.00003564568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001364903,"about_ca_topic_score_gemma":0.00001244929,"domain_scores_codex":[0.9980517,0.00004752354,0.0005475681,0.0006022801,0.0001676036,0.000583306],"domain_scores_gemma":[0.9989074,0.0004131576,0.0001318025,0.0003793194,0.00007103771,0.00009729059],"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":[0.00002511784,0.00003383939,0.00001309762,0.0004886652,0.0003333737,0.000004616244,0.00009996362,0.8681147,0.002093522,0.009219061,0.001180432,0.1183937],"study_design_scores_gemma":[0.0004625144,0.00004402041,0.0004003669,0.0002963272,0.00007351076,0.00001474635,0.0001249027,0.649891,0.3464825,0.0007128489,0.001120515,0.0003767944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01069048,0.0002364859,0.9840971,0.000156236,0.002228889,0.000664659,0.00006102859,0.00167642,0.0001887502],"genre_scores_gemma":[0.8498909,0.0001413278,0.1487524,0.000100767,0.0002291682,0.0001122605,0.0002464202,0.00009032476,0.0004363651],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8392005,"threshold_uncertainty_score":0.9997176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0124868171837742,"score_gpt":0.2315390319323142,"score_spread":0.21905221474854,"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."}}