{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004385771,0.0006368819,0.0004743899,0.0003485772,0.00026838,0.0004815531,0.001047199,0.000670508,0.0007589571],"category_scores_gemma":[0.0009944149,0.0003311299,0.0003431729,0.0004201369,0.0003628575,0.0008942846,0.000618865,0.0004834933,0.0002666533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000561467,"about_ca_system_score_gemma":0.0007552585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005876116,"about_ca_topic_score_gemma":0.003260474,"domain_scores_codex":[0.9994673,0.00004093863,0.00002910632,0.0001261716,0.0002961108,0.00004039768],"domain_scores_gemma":[0.9997106,0.0000773361,0.0000595551,0.00004464131,0.00009756975,0.0000102097],"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":[0.00006767517,0.00002682931,0.001126477,0.00017274,0.00002238212,0.0001962677,0.0001384389,0.8941854,0.05123056,0.008136879,0.0004133795,0.04428302],"study_design_scores_gemma":[0.000003446666,0.00002749853,0.0004513181,0.000004675073,0.000005455329,0.00005185383,0.00000630082,0.989791,0.00817049,0.000561804,0.0009153008,0.00001091262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02409102,0.0004423908,0.9728415,0.00006380122,0.00003113892,0.00002627266,0.00004673436,0.0003788801,0.002078261],"genre_scores_gemma":[0.8176749,0.00115762,0.1727792,0.00005123485,0.00003224652,0.0001625347,0.0001983505,0.0001616205,0.007782303],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005876116,"threshold_uncertainty_score":0.01168382,"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."}}