{"id":"W2279877162","doi":"10.1115/detc2015-46047","title":"Considering Machining Tolerances in High Speed Corner Tracking","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Numerical Analysis Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Trajectory; Machining; Acceleration; Tracking (education); Trajectory optimization; Numerical control; Computer science; Limit (mathematics); Control theory (sociology); Machine tool; Optimization problem; Engineering; Algorithm; Mechanical engineering; Control (management); Mathematics; Artificial intelligence","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":[0.0001188004,0.000120429,0.0002252753,0.0001057819,0.00001347183,0.00002494759,0.0000905921,0.00004204066,0.00005711613],"category_scores_gemma":[0.0000401043,0.000109769,0.00003089948,0.0002596189,0.0000225703,0.0002294428,0.00002365046,0.000157428,0.00002418321],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007080086,"about_ca_system_score_gemma":0.000005575117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001137649,"about_ca_topic_score_gemma":0.00008552698,"domain_scores_codex":[0.9992895,0.00001099943,0.0002299764,0.0001403189,0.0001196172,0.0002095624],"domain_scores_gemma":[0.9997196,0.00003996922,0.00001945214,0.0001231564,0.00002227808,0.0000754958],"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":[0.00001885437,0.00004277489,0.05466171,0.00004138321,0.00005808311,0.0001090658,0.0008275096,0.8523985,0.01341377,0.002680504,0.001120704,0.07462718],"study_design_scores_gemma":[0.001543386,0.00008430015,0.009854673,0.0001789,0.00003401264,0.0000337372,0.001016224,0.8195397,0.1328208,0.02486097,0.008712898,0.001320412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8532008,0.0004062743,0.1188012,0.00009547303,0.0001514938,0.0001035704,9.009917e-7,0.001567962,0.02567229],"genre_scores_gemma":[0.965852,0.00001566729,0.03391638,0.00007847239,0.00003787747,0.00000456576,0.000001921017,0.00002274432,0.00007042707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.119407,"threshold_uncertainty_score":0.447625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03186453164039806,"score_gpt":0.2624652968313377,"score_spread":0.2306007651909396,"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."}}