{"id":"W3025342261","doi":"10.1109/vrw50115.2020.00165","title":"Map Displays And Landmark Effects On Wayfinding In Unfamiliar Environments","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Landmark; Computer science; Track (disk drive); Computer vision; Scale (ratio); Artificial intelligence; Virtual reality; Human–computer interaction; Computer graphics (images); Cartography; Geography","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":[0.0001342037,0.0002457967,0.000261309,0.00004502751,0.00007179355,0.0001304244,0.00007340715,0.0001611794,0.00004141654],"category_scores_gemma":[0.00004533318,0.0002249535,0.00001972646,0.00006699303,0.00006581746,0.0001752931,0.00003453991,0.0004298713,0.0000353293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002501938,"about_ca_system_score_gemma":0.000006810759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004572328,"about_ca_topic_score_gemma":0.00008515557,"domain_scores_codex":[0.9989417,0.00005174034,0.0002724173,0.0003541796,0.0001608602,0.0002191598],"domain_scores_gemma":[0.9994088,0.0002193903,0.00005122873,0.00009681192,0.000008591887,0.0002151904],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003479613,0.000528576,0.01334226,0.00217486,0.0004269067,0.0003665902,0.01918668,0.03362088,0.1160346,0.005160626,0.007337979,0.7983404],"study_design_scores_gemma":[0.0101103,0.004265944,0.5960509,0.0062956,0.0002353559,0.00002033674,0.004275061,0.2180846,0.1420188,0.002036834,0.01321458,0.003391688],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967707,0.0001242213,0.0003907244,0.001051429,0.0002019787,0.0002276053,0.00004221504,0.00005312764,0.00113796],"genre_scores_gemma":[0.9978663,0.00146158,0.00001873837,0.0004466865,0.00008106649,0.00001356712,0.00003175851,0.00001889133,0.00006139222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7949488,"threshold_uncertainty_score":0.9173337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02669056135754473,"score_gpt":0.2487285105676957,"score_spread":0.2220379492101509,"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."}}