{"id":"W2128967361","doi":"10.5194/isprsarchives-xxxix-b4-231-2012","title":"VISION-AIDED CONTEXT-AWARE FRAMEWORK FOR PERSONAL NAVIGATION SERVICES","year":2012,"lang":"en","type":"article","venue":"˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Turn-by-turn navigation; Context (archaeology); Global Positioning System; Heading (navigation); Navigation system; Computer vision; Mobile device; Mobile robot navigation; Human–computer interaction; Dead reckoning; Location-based service; Orientation (vector space); Mobile phone; Kalman filter; Map matching; Artificial intelligence; Real-time computing; Mobile robot; Engineering; World Wide Web; Telecommunications","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":[0.0003193186,0.000470101,0.0004977123,0.0005387132,0.0003624187,0.0008793646,0.0008393507,0.0008102406,0.002099056],"category_scores_gemma":[0.0006156617,0.0002336305,0.000600107,0.0004072484,0.0002371132,0.0009506226,0.0007231158,0.000747646,0.0007103406],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005926422,"about_ca_system_score_gemma":0.001000257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01765544,"about_ca_topic_score_gemma":0.01518965,"domain_scores_codex":[0.9997912,0.00003569927,0.00001415295,0.00005138671,0.00007263575,0.00003491223],"domain_scores_gemma":[0.9998754,0.00002224728,0.00001092063,0.00001783204,0.00005747333,0.00001602816],"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":[0.0004475698,0.0003949352,0.002223044,0.0004399209,0.000198039,0.0006790042,0.000469352,0.2235497,0.08596103,0.04013931,0.01236442,0.6331337],"study_design_scores_gemma":[0.00002711493,0.00007080355,0.0006001812,0.00003204618,0.0000446497,0.0001547544,0.00006246015,0.9720147,0.009075193,0.006363894,0.01152332,0.00003082333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01350652,0.0007744303,0.979251,0.0001095592,0.00006961659,0.00008416525,0.0000997242,0.003248069,0.00285683],"genre_scores_gemma":[0.5004932,0.0007381049,0.4940677,0.0001277557,0.00004906985,0.0001764784,0.0004001784,0.0000864373,0.00386118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01765544,"threshold_uncertainty_score":0.03510535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01374274002275344,"score_gpt":0.2581822676305907,"score_spread":0.2444395276078373,"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."}}