{"id":"W2889785251","doi":"10.5194/isprs-archives-xlii-4-253-2018","title":"INDOOR POSITIONING USING WLAN FINGERPRINT MATCHING AND PATH ASSESSMENT WITH RETROACTIVE ADJUSTMENT ON MOBILE DEVICES","year":2018,"lang":"en","type":"article","venue":"The international 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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Dead reckoning; Fingerprint (computing); Computer science; Real-time computing; Fingerprint recognition; Pedestrian; Matching (statistics); Indoor positioning system; Hybrid positioning system; Path (computing); Inertial measurement unit; Mobile device; Sensor fusion; Artificial intelligence; Positioning system; Computer vision; Global Positioning System; Accelerometer; Telecommunications; Computer network; Engineering; Transport engineering","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":[0.0004232074,0.0006993588,0.0006148677,0.001105551,0.0002976124,0.0006106138,0.0008294612,0.0005531766,0.001280188],"category_scores_gemma":[0.001364463,0.0002207486,0.0004191543,0.00133283,0.0001854272,0.0008156127,0.0007838017,0.0003385641,0.0009191114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002874224,"about_ca_system_score_gemma":0.0002904756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00145061,"about_ca_topic_score_gemma":0.001699979,"domain_scores_codex":[0.9992331,0.0001577007,0.00003573642,0.000181497,0.0003145085,0.00007748206],"domain_scores_gemma":[0.9995149,0.00006846261,0.00009216017,0.0001266475,0.0001781314,0.00001971584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006150396,0.0001628175,0.01049173,0.0002168062,0.0001414136,0.0002909651,0.0001973563,0.02977418,0.1764162,0.00127431,0.001521968,0.7788972],"study_design_scores_gemma":[0.00007623118,0.001367205,0.03106906,0.00005839182,0.0002563392,0.002043569,0.0002142682,0.7021455,0.2494735,0.001377438,0.0117457,0.0001729649],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1448709,0.0004055924,0.8483999,0.00007750491,0.0001362102,0.00009710082,0.0001293085,0.002913094,0.002970309],"genre_scores_gemma":[0.7977277,0.0001972509,0.1995193,0.00005150064,0.00003271043,0.00006248955,0.0001360365,0.00004903486,0.002223856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00145061,"threshold_uncertainty_score":0.004282653,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01199423400727732,"score_gpt":0.2524329836482493,"score_spread":0.2404387496409719,"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."}}