{"id":"W2899811153","doi":"10.1007/s00221-018-5417-x","title":"Selective resetting position and heading estimations while driving in a large-scale immersive virtual environment","year":2018,"lang":"en","type":"article","venue":"Experimental Brain Research","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heading (navigation); Computer vision; Position (finance); Artificial intelligence; Motion (physics); Displacement (psychology); Computer science; Landmark; Scale (ratio); Path integration; Virtual reality; Orientation (vector space); Path (computing); Computer graphics (images); Communication; Psychology; Geodesy; Mathematics; Geography; Geometry; Cartography","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":[0.0001674917,0.000248254,0.0002065254,0.000218394,0.0001149269,0.0004003744,0.0002166469,0.0002169075,0.001033781],"category_scores_gemma":[0.003029566,0.0001896362,0.00009368557,0.0001553806,0.0002974663,0.0002218908,0.0003776682,0.000289149,0.0001193134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008107326,"about_ca_system_score_gemma":0.0002221661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001997133,"about_ca_topic_score_gemma":0.002378257,"domain_scores_codex":[0.9998581,0.0000311562,0.000009311434,0.00003655977,0.00003109965,0.00003373782],"domain_scores_gemma":[0.9992595,0.0003339908,0.0001067,0.00009768481,0.00008449443,0.0001176475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006901875,0.0007350459,0.05086444,0.0002101254,0.000194921,0.0003777487,0.00193011,0.004495691,0.8770116,0.0002940172,0.0006929747,0.05629152],"study_design_scores_gemma":[0.0001410673,0.002405284,0.9221513,0.00002843552,0.0001492964,0.0007219747,0.0009290026,0.02071729,0.05133675,0.0005403315,0.0008208445,0.00005835802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987645,0.0000208093,0.0009333723,0.000009058117,0.0000101298,0.000006562695,0.0000546949,0.00001840848,0.0001824438],"genre_scores_gemma":[0.9993473,0.00001994943,0.0003915295,0.000009992322,0.000003862968,0.000005817685,0.00005526888,0.00001238426,0.0001540737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001997133,"threshold_uncertainty_score":0.003970981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540138967646726,"score_gpt":0.3386395327645372,"score_spread":0.31323814308807,"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."}}