{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002342675,0.0007695862,0.0019617,0.0003003779,0.0001364736,0.0001616809,0.001263025,0.0009645461,0.00006102601],"category_scores_gemma":[0.001431545,0.0007314456,0.0003018115,0.0008251627,0.00008301011,0.0000947826,0.0004108172,0.0007297666,0.0003220204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002432426,"about_ca_system_score_gemma":0.000127248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000911038,"about_ca_topic_score_gemma":0.0003126309,"domain_scores_codex":[0.9961999,0.001185834,0.001098537,0.0006100537,0.0002763401,0.0006293306],"domain_scores_gemma":[0.9968156,0.001350539,0.0003426344,0.001216439,0.0001934234,0.00008132942],"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":[8.954049e-7,0.00001496181,0.0004547638,0.004515884,0.0001493074,0.000001641856,0.00002299922,0.0004654499,5.52168e-7,0.00001005251,0.006859385,0.9875041],"study_design_scores_gemma":[0.0004898784,0.00006322112,0.0002200809,0.002960959,0.0002032325,0.00001023864,0.00001450326,0.0582455,0.00004923657,0.00001796314,0.9363028,0.00142242],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001325784,0.6641861,0.3317396,0.000001874521,0.001671446,0.0006724795,0.0002834904,0.001316164,0.0001155483],"genre_scores_gemma":[0.01309636,0.9814349,0.0001681986,0.00003465702,0.0001792864,0.00002218575,0.004610936,0.0002860956,0.0001673202],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9860817,"threshold_uncertainty_score":0.9995137,"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."}}