{"id":"W4245724764","doi":"10.32920/ryerson.14662803","title":"Pose estimation for robotic percussive riveting.","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; FedDev Ontario","keywords":"Rivet; Robot; Pose; Engineering; Position (finance); Artificial intelligence; Computer science; Computer vision; Simulation; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000668806,0.001295389,0.0008372563,0.0006324674,0.0003435642,0.0005716343,0.0009075713,0.000765923,0.002521873],"category_scores_gemma":[0.002051204,0.0005106856,0.0006638245,0.0005168021,0.0004899914,0.0007655552,0.001103941,0.0008851818,0.001666474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003322642,"about_ca_system_score_gemma":0.0007901588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002387812,"about_ca_topic_score_gemma":0.002634686,"domain_scores_codex":[0.9992195,0.0001718871,0.00002579602,0.000245945,0.0002691532,0.00006752279],"domain_scores_gemma":[0.9992632,0.000173746,0.0001656714,0.0001356487,0.0002262886,0.00003532242],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002643912,0.00009175449,0.003097587,0.0002235335,0.0001118368,0.0002279192,0.0002339976,0.3385687,0.04247209,0.006036227,0.003591924,0.6050801],"study_design_scores_gemma":[0.00001505855,0.0002451781,0.00401683,0.00002774622,0.0000242065,0.0002834669,0.0001249984,0.9676888,0.01439844,0.004727493,0.008401382,0.00004634283],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00436839,0.0001582365,0.9942635,0.00002755275,0.00002693336,0.00002375399,0.00003699182,0.0003621963,0.0007324574],"genre_scores_gemma":[0.497129,0.001127297,0.4913981,0.0001866978,0.0001710344,0.0002733438,0.001414205,0.0002332621,0.008067027],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002521873,"threshold_uncertainty_score":0.008436501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370201054388275,"score_gpt":0.2709049863755139,"score_spread":0.2472029758316312,"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."}}