{"id":"W2950552589","doi":"10.1002/acs.2887","title":"Transfer learning for high‐precision trajectory tracking through adaptive feedback and iterative learning","year":2018,"lang":"en","type":"article","venue":"International Journal of Adaptive Control and Signal Processing","topic":"Iterative Learning Control Systems","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Alfred P. Sloan Foundation","keywords":"Trajectory; Iterative learning control; Control theory (sociology); Controller (irrigation); Tracking (education); Computer science; Parametric statistics; Adaptive control; Adaptation (eye); Control engineering; Control (management); Artificial intelligence; Mathematics; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008286108,0.0005628258,0.0004119649,0.0003713763,0.0003534334,0.0004874392,0.0009345199,0.0006279908,0.001407592],"category_scores_gemma":[0.002271193,0.0002251579,0.0003649644,0.0002799472,0.0009011381,0.0006083463,0.001101684,0.0009423654,0.0002803253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006897982,"about_ca_system_score_gemma":0.0007871017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00459457,"about_ca_topic_score_gemma":0.002611598,"domain_scores_codex":[0.9995589,0.00008873699,0.00002470511,0.0000843602,0.0001809558,0.00006239849],"domain_scores_gemma":[0.9990706,0.0004091665,0.0001234137,0.0001159404,0.0002421347,0.00003880733],"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":[0.00007291109,0.00008929983,0.0006343255,0.00007262504,0.00003562694,0.0001054585,0.0001702539,0.8563854,0.01587617,0.008511189,0.0006378505,0.1174089],"study_design_scores_gemma":[0.000003412252,0.00002517524,0.00006769801,0.000002381518,0.000001651236,0.000009508727,0.000003796484,0.9968318,0.001677588,0.001192925,0.0001808332,0.000003211551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02120942,0.00008029861,0.9762353,0.00005422866,0.0000144649,0.00003097332,0.000004674662,0.0003926083,0.001978072],"genre_scores_gemma":[0.933825,0.0000533442,0.06429188,0.00004342796,0.00001376054,0.00009707168,0.00001738765,0.00003630238,0.001621853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00459457,"threshold_uncertainty_score":0.009135664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01594862994345886,"score_gpt":0.250022259816716,"score_spread":0.2340736298732571,"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."}}