{"id":"W4251296224","doi":"10.32920/ryerson.14662803.v1","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":"","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":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001160712,0.0001913161,0.0002574763,0.00008833442,0.00003109823,0.00002924449,0.0001095609,0.000252681,0.00005026497],"category_scores_gemma":[0.0001109463,0.0001892425,0.0001127699,0.00003760294,0.00001558991,0.00005929587,0.00007337827,0.0002668983,0.000003356394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001038311,"about_ca_system_score_gemma":0.00002680764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002904415,"about_ca_topic_score_gemma":0.000007545781,"domain_scores_codex":[0.9993156,0.00001184872,0.0001849263,0.0002197369,0.00008412412,0.0001837528],"domain_scores_gemma":[0.9995375,0.00004292974,0.00003833875,0.0002335217,0.0001102982,0.00003739631],"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":[0.00000763276,0.00002323004,0.00007885128,0.0006432416,0.000135161,0.00000467919,0.0002120533,0.96229,0.01339211,0.0005342593,0.002865263,0.01981346],"study_design_scores_gemma":[0.0004036232,0.00006955952,0.0004912181,0.00036629,0.0002332561,0.000007259811,0.0001421338,0.8949152,0.08806238,0.01346096,0.001074454,0.0007736459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005125904,0.0007153677,0.9879423,0.00007907306,0.000816895,0.0005207339,0.00000300622,0.001040876,0.00375579],"genre_scores_gemma":[0.5181766,0.0001292732,0.4807499,0.00004874063,0.0001160106,0.0002784738,0.000184041,0.00004337991,0.0002735977],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5130507,"threshold_uncertainty_score":0.7717087,"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."}}