{"id":"W4362469901","doi":"10.3390/mi14040779","title":"Modeling and Compensation of Positioning Error in Micromanipulation","year":2023,"lang":"en","type":"article","venue":"Micromachines","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Compensation (psychology); Image stitching; Displacement (psychology); Distortion (music); Nonlinear system; Computer science; Computer vision; Translation (biology); Nonlinear distortion; Approximation error; Artificial intelligence; Control theory (sociology); Algorithm; Physics","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":[0.00006308203,0.00004702255,0.00006253448,0.0001145351,0.0000296116,0.00001221938,0.00003108522,0.00002420687,0.000001382404],"category_scores_gemma":[0.00000327417,0.00005103304,0.000009271792,0.0001930557,0.000008953803,0.0000668552,0.00001310094,0.00003904168,0.000002273095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009436701,"about_ca_system_score_gemma":0.000001957792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006180237,"about_ca_topic_score_gemma":0.00001409731,"domain_scores_codex":[0.9997228,0.000004355681,0.0001223531,0.00006169385,0.00002631641,0.00006241908],"domain_scores_gemma":[0.9999095,0.000008193611,0.00001090102,0.00004982216,0.00001377686,0.000007845172],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001423,0.000006173976,0.001555663,0.00009206643,0.000002798702,5.414188e-7,0.0003618799,0.132917,0.8613686,0.0003536736,0.00005146985,0.003288719],"study_design_scores_gemma":[0.00006979875,0.00000277,0.007002643,0.00004354361,0.000002526559,0.00000294595,0.00002073763,0.9768822,0.01418982,0.001715885,0.00001435834,0.00005278758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9569526,0.0001455092,0.04236384,0.00006798009,0.00001298947,0.00006306954,0.000003269888,0.0002503643,0.0001403712],"genre_scores_gemma":[0.988905,0.00002255965,0.01099113,0.000007123673,0.00000877043,0.000009940758,0.00003746854,0.00001096624,0.000007032586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8471788,"threshold_uncertainty_score":0.2081067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969047121786441,"score_gpt":0.2682511116684831,"score_spread":0.2485606404506187,"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."}}