{"id":"W4406517247","doi":"10.2139/ssrn.5101342","title":"Adaptive Longitudinal Slip Compensation for Wheeled Mobile Robots Using Velocity Ratio","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Control and Dynamics of Mobile Robots","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Slip (aerodynamics); Robot; Compensation (psychology); Mobile robot; Control theory (sociology); Computer science; Geology; Artificial intelligence; Engineering; Psychology; Aerospace engineering; Control (management); Social psychology","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001091828,0.0004883893,0.0006716479,0.0002934725,0.0002955085,0.0001659554,0.0004473393,0.0003975417,0.00001405785],"category_scores_gemma":[0.00004340675,0.0005278378,0.0004407992,0.0001400853,0.00003369889,0.0001814109,0.0001593407,0.003425171,0.000004416183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00371143,"about_ca_system_score_gemma":0.002840634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009134145,"about_ca_topic_score_gemma":0.00131822,"domain_scores_codex":[0.9966301,0.00007358413,0.000633406,0.0004155604,0.0002915176,0.001955811],"domain_scores_gemma":[0.9988428,0.0001413981,0.000258981,0.0003163986,0.0003369391,0.0001035253],"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":[0.0001146802,0.00003782718,0.0000868336,0.0001036431,0.0007235236,0.000001979258,0.00007283399,0.9675812,0.0001770554,0.01005081,0.00004508861,0.02100456],"study_design_scores_gemma":[0.001191682,0.000157894,0.0002475385,0.0001974887,0.0002825558,0.00005982829,0.0001617497,0.9198089,0.00003507537,0.07730722,0.00007797789,0.0004721572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05145567,0.006352485,0.9388899,0.00005766789,0.001542175,0.001188505,0.00005920933,0.0001784763,0.0002758932],"genre_scores_gemma":[0.9905975,0.002218833,0.005430406,0.00001696444,0.0008765807,0.0002092794,0.00008933803,0.00006908856,0.0004920305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9391418,"threshold_uncertainty_score":0.9997173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01748117543478277,"score_gpt":0.2569876337899299,"score_spread":0.2395064583551472,"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."}}