{"id":"W4232697545","doi":"10.32920/ryerson.14646243","title":"Robotic tooling calibration based on linear and nonlinear formulations","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":"","keywords":"Calibration; Robot calibration; Robot; Nonlinear system; Position (finance); Computer science; Industrial robot; Artificial intelligence; Robotics; Kinematics; Control engineering; Simulation; Computer vision; Robot kinematics; Engineering; Mobile robot; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008736219,0.0007223881,0.0003568252,0.0005398963,0.000241602,0.0008879344,0.0007065891,0.0007353948,0.002006267],"category_scores_gemma":[0.001813504,0.0004133222,0.0005910193,0.000475838,0.0008983202,0.001371378,0.001030962,0.001153513,0.0007434846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006084481,"about_ca_system_score_gemma":0.0005922689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001015507,"about_ca_topic_score_gemma":0.001059524,"domain_scores_codex":[0.999079,0.0001753336,0.00004558673,0.0002061882,0.0004621976,0.00003162058],"domain_scores_gemma":[0.9993148,0.0002199319,0.0001120678,0.0001102138,0.0002327823,0.00001016078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006644274,0.00008586234,0.001041793,0.0009901638,0.00006923402,0.0002177026,0.0005020464,0.4627097,0.08165073,0.1857268,0.002101135,0.2648384],"study_design_scores_gemma":[0.000007338952,0.00007124899,0.0004991984,0.00005249044,0.00002002093,0.0001823896,0.00003550546,0.9597995,0.01702797,0.0150578,0.007217533,0.00002903271],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003174906,0.0002630803,0.9928194,0.00003990694,0.0000201701,0.00002249639,0.00001404678,0.00007857936,0.003567434],"genre_scores_gemma":[0.4239303,0.00219999,0.5551029,0.0002067508,0.0001080143,0.0002424751,0.000164863,0.0002808728,0.01776388],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002006267,"threshold_uncertainty_score":0.006711602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02897318214897772,"score_gpt":0.2638660219319381,"score_spread":0.2348928397829604,"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."}}