{"id":"W2953529749","doi":"10.1145/3322241","title":"Indoor Localization Improved by Spatial Context—A Survey","year":2019,"lang":"en","type":"review","venue":"ACM Computing Surveys","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"China Scholarship Council","keywords":"Computer science; Spatial contextual awareness; Context (archaeology); Doors; Computer vision; Spatial analysis; Artificial intelligence; Location-based service; Remote sensing; Telecommunications; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007989553,0.001299525,0.001320085,0.002262367,0.0004119117,0.001268639,0.001865212,0.00106151,0.005394859],"category_scores_gemma":[0.002510447,0.0005875343,0.0009097518,0.004682447,0.0005483619,0.002778482,0.001324392,0.0008072305,0.003240693],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005049995,"about_ca_system_score_gemma":0.0009061029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004215388,"about_ca_topic_score_gemma":0.003649881,"domain_scores_codex":[0.9991893,0.0001881553,0.00005674918,0.0001805335,0.0003048827,0.00008034278],"domain_scores_gemma":[0.9983706,0.0008773774,0.00008688613,0.0001287684,0.0004972865,0.0000392137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003900905,0.00005025824,0.0009213035,0.005098666,0.00005834396,0.0001193518,0.0001129685,0.004783109,0.001186917,0.008166054,0.01503252,0.9644316],"study_design_scores_gemma":[0.00002832524,0.0003870466,0.004878421,0.005490177,0.0005000689,0.003399097,0.001051463,0.03143811,0.005732935,0.01420982,0.9326962,0.0001883228],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.004609229,0.8043858,0.162691,0.001260743,0.0009485308,0.00007447408,0.0002514462,0.0008054512,0.02497339],"genre_scores_gemma":[0.06186394,0.8829582,0.04382414,0.0005621364,0.001076649,0.00009003642,0.0006414911,0.0001527714,0.008830684],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005394859,"threshold_uncertainty_score":0.01804757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04414567685770718,"score_gpt":0.290293049038672,"score_spread":0.2461473721809649,"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."}}