{"id":"W1999843357","doi":"10.1109/coase.2010.5584643","title":"Automated surface deformations detection and marking on automotive body panels","year":2010,"lang":"en","type":"article","venue":"","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Automotive industry; Context (archaeology); Surface (topology); Computer science; Computer vision; Artificial intelligence; Engineering; Automotive 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":[],"consensus_categories":[],"category_scores_codex":[0.0002102463,0.0000769219,0.00006529259,0.00002246016,0.0002544435,0.00007739235,0.00004822209,0.00005760222,0.0008791754],"category_scores_gemma":[0.0000396383,0.00005239751,0.00001680001,0.0001061059,0.00003227114,0.0002135476,0.000003618332,0.0001506626,0.0002636507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000001504534,"about_ca_system_score_gemma":0.000007167324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001056733,"about_ca_topic_score_gemma":0.004276625,"domain_scores_codex":[0.9994984,0.0000450826,0.00009211936,0.0001200435,0.00009819465,0.0001461967],"domain_scores_gemma":[0.9997087,0.00008779305,0.00002782269,0.00007313009,0.00003077617,0.0000717406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007888843,0.00005215018,0.7502961,0.00004127056,0.00005265685,0.0000167194,0.002011594,0.004655953,0.03080311,0.000172785,0.0006102279,0.2112085],"study_design_scores_gemma":[0.00008201825,0.00006027075,0.8520218,0.000004448474,0.000003071146,0.00001831554,0.000185039,0.144982,0.001883055,0.00004481546,0.0006245059,0.00009066884],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9720895,0.000008760549,0.00005677067,0.00006168221,0.0002883893,0.00007344825,0.0000168034,0.0004224901,0.02698211],"genre_scores_gemma":[0.9988029,0.000004676108,0.000349661,0.000093694,0.0000238214,2.652178e-7,0.00002604939,0.000001560828,0.0006973867],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2111179,"threshold_uncertainty_score":0.9626355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228544704169884,"score_gpt":0.2159566968208822,"score_spread":0.2036712497791834,"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."}}