{"id":"W2057047210","doi":"10.1145/1870076.1870077","title":"Modeling locomotor control","year":2011,"lang":"en","type":"article","venue":"ACM Transactions on Applied Perception","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Engineering and Physical Sciences Research Council","keywords":"Gaze; Computer science; Control (management); Artificial intelligence; Visual control; Computer vision; Human–computer interaction","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":[0.0001242507,0.0001413482,0.0001327128,0.0001781893,0.0002050503,0.0000310169,0.0006760697,0.0001261467,0.0001958605],"category_scores_gemma":[0.000003629123,0.0001373808,0.0000718192,0.0002100639,0.00004470599,0.000147653,0.000007181972,0.0002634915,0.0005398626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000614844,"about_ca_system_score_gemma":0.00001731377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004733592,"about_ca_topic_score_gemma":0.00001413007,"domain_scores_codex":[0.999065,0.00002260371,0.0001716013,0.0003713762,0.0001424842,0.000226969],"domain_scores_gemma":[0.9991075,0.00002224598,0.00003011713,0.0007434381,0.00003875097,0.00005799641],"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":[0.00008686132,0.0005881965,0.00004354501,0.00001104689,0.00005203158,0.000004452529,0.002665826,0.009151873,0.02908483,0.01954299,0.00003200684,0.9387363],"study_design_scores_gemma":[0.003777053,0.0006860229,0.01673402,0.00004827173,0.0001118448,0.00005397237,0.001377331,0.9128778,0.007841675,0.0546884,0.0005647594,0.001238841],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05298568,0.000002201381,0.9435772,0.0002855837,0.0001458971,0.0001811583,0.000002716308,0.0007142583,0.00210534],"genre_scores_gemma":[0.9096383,0.00000780084,0.08990987,0.0002648398,0.00001910697,0.00009657354,0.000001145232,0.00001120597,0.00005117443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9374975,"threshold_uncertainty_score":0.6939021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03883479809623689,"score_gpt":0.2374088126794607,"score_spread":0.1985740145832238,"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."}}