{"id":"W4402679196","doi":"10.7554/elife.95764.2","title":"Shortcutting from self-motion signals: quantifying trajectories and active sensing in an open maze","year":2024,"lang":"en","type":"preprint","venue":"eLife","topic":"Neural dynamics and brain function","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Motion (physics); Computer vision; Artificial intelligence","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":[0.000244765,0.0002797137,0.0001923984,0.0004045256,0.00009330052,0.0003121003,0.0003177623,0.0002748053,0.0004535876],"category_scores_gemma":[0.001611255,0.0001399846,0.0001400139,0.0001850697,0.0004036562,0.0004821777,0.0004030771,0.0003249079,0.00006565519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002183305,"about_ca_system_score_gemma":0.0001286298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008291324,"about_ca_topic_score_gemma":0.0009044067,"domain_scores_codex":[0.9999334,0.00001262314,0.000003094794,0.00001644672,0.0000250743,0.000009464427],"domain_scores_gemma":[0.9994894,0.0001893251,0.000165072,0.00004389676,0.00004905699,0.00006321097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0005359101,0.0001466699,0.03325514,0.0001491877,0.0001155355,0.0002976677,0.0003006866,0.1821646,0.7101861,0.0089634,0.0002785023,0.06360655],"study_design_scores_gemma":[0.00001079645,0.0002285189,0.04364011,0.00001158687,0.00001271494,0.0001452345,0.0000631898,0.8889853,0.05836901,0.008220502,0.00028155,0.00003150973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9058173,0.00005510293,0.09357089,0.00002570205,0.00000471402,0.000008564265,0.00009173121,0.0001062575,0.0003197319],"genre_scores_gemma":[0.9869505,0.00003216837,0.01270683,0.000004705679,0.000002023804,0.000007390742,0.00005811306,0.00001646257,0.0002219452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008291324,"threshold_uncertainty_score":0.001648664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1091394221300773,"score_gpt":0.3480252363938018,"score_spread":0.2388858142637245,"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."}}