{"id":"W4411359001","doi":"10.1109/tfr.2025.3580397","title":"DRIVE Through the Unpredictability: From a Protocol Investigating Slip to a Metric Estimating Command Uncertainty","year":2025,"lang":"en","type":"article","venue":"IEEE transactions on field robotics.","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"SNC-Lavalin (Canada); Université Laval","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Metric (unit); Protocol (science); Computer science; Slip (aerodynamics); Engineering; Aerospace engineering; Operations management; Medicine","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":[0.003620608,0.0007012252,0.0005582378,0.0007662972,0.0005019188,0.001005035,0.001140175,0.0008146181,0.0009356657],"category_scores_gemma":[0.0197289,0.0003151715,0.0002907609,0.0004667528,0.001297598,0.001689082,0.002327648,0.001135873,0.0004193969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007506723,"about_ca_system_score_gemma":0.001454149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002734132,"about_ca_topic_score_gemma":0.002776289,"domain_scores_codex":[0.9973924,0.001004096,0.0001961145,0.0004594675,0.0007523182,0.0001956167],"domain_scores_gemma":[0.9926615,0.003069301,0.0008444125,0.001839224,0.001223025,0.0003625916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002764375,0.001040565,0.06840699,0.0009551093,0.000266173,0.001091562,0.001758957,0.3868658,0.08898554,0.02400803,0.008986975,0.4148699],"study_design_scores_gemma":[0.0001407738,0.002984796,0.0234333,0.0001520949,0.0000534986,0.000521232,0.001183473,0.8884032,0.05404832,0.01813889,0.01078293,0.0001574866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4259805,0.000351778,0.5626152,0.0006940571,0.0001426485,0.001281754,0.001417053,0.003191728,0.004325289],"genre_scores_gemma":[0.8745958,0.0001426906,0.1206283,0.0001604188,0.00002501623,0.001230583,0.001836519,0.0002178803,0.001162948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003620608,"threshold_uncertainty_score":0.01914781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09265760901618103,"score_gpt":0.4224538347919273,"score_spread":0.3297962257757463,"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."}}