{"id":"W2098914344","doi":"10.1115/1.4000959","title":"Adaptive Control of Pressure Tracking for Polishing Process","year":2010,"lang":"en","type":"article","venue":"Journal of Manufacturing Science and Engineering","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Toronto Metropolitan University","funders":"","keywords":"Control theory (sociology); Polishing; Controller (irrigation); Constant (computer programming); Tracking (education); Estimator; Figuring; Process (computing); Adaptive control; Computer science; Control engineering; Engineering; Control (management); Mathematics; Mechanical engineering; 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":[0.000889502,0.0001334424,0.0002487621,0.0002732236,0.00006689127,0.00007971984,0.0003486052,0.00006291166,0.000001358118],"category_scores_gemma":[0.0004055239,0.0001207189,0.0000494124,0.000119048,0.00009087723,0.001308279,0.00001899648,0.0004094085,5.190245e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002714112,"about_ca_system_score_gemma":0.00004374896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002581876,"about_ca_topic_score_gemma":0.000001113808,"domain_scores_codex":[0.9989694,0.000002623416,0.0003137662,0.0001061269,0.0003209982,0.0002870716],"domain_scores_gemma":[0.9992708,0.0001286628,0.0001328816,0.0001098436,0.0002445927,0.0001131987],"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":[0.0000192284,0.000009049307,0.0001394735,0.0002954431,0.00004065413,0.000002642997,0.0007892809,0.5762569,0.4145377,0.0002927064,0.00002309324,0.007593824],"study_design_scores_gemma":[0.0003370284,0.00006957103,0.002357523,0.0001636837,0.00003380832,0.00006022782,0.0001099064,0.1033966,0.8920678,0.000607939,0.0006211408,0.000174779],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.820192,0.000225572,0.1788346,0.00003428855,0.0004335914,0.0001237468,0.000006718097,0.00009402842,0.00005548549],"genre_scores_gemma":[0.9713772,0.00001551434,0.02843875,0.000007473946,0.000132763,0.000003656055,6.962527e-8,0.00002280031,0.000001786442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4775301,"threshold_uncertainty_score":0.4922775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008103239614873282,"score_gpt":0.2389667404364386,"score_spread":0.2308635008215653,"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."}}