{"id":"W2152034016","doi":"10.1186/2192-1962-3-2","title":"Enhancing Wi-Fi fingerprinting for indoor positioning using human-centric collaborative feedback","year":2013,"lang":"en","type":"article","venue":"Human-centric Computing and Information Sciences","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Regina; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Global Positioning System; Baseline (sea); Context (archaeology); Mobile device; Hybrid positioning system; Bookmarking; Key (lock); Human–computer interaction; Indoor positioning system; Positioning system; Real-time computing; Computer security; World Wide Web; Telecommunications; Node (physics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001277417,0.0009473875,0.001160599,0.0005864302,0.0004943685,0.0007644982,0.001501824,0.00125117,0.0008383876],"category_scores_gemma":[0.004496476,0.0003588558,0.000427454,0.0005837971,0.0005225923,0.001278543,0.001055693,0.0007030074,0.0005948687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000597048,"about_ca_system_score_gemma":0.0007423544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005081348,"about_ca_topic_score_gemma":0.005108193,"domain_scores_codex":[0.998539,0.0003562307,0.00005495104,0.0003272859,0.0005557321,0.000166733],"domain_scores_gemma":[0.9975618,0.0008573164,0.0003193028,0.0005182366,0.0006245692,0.0001187441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000664178,0.0005214036,0.006101142,0.0001877577,0.0001362248,0.0003938079,0.0002492351,0.5573227,0.1091425,0.003173785,0.002021847,0.3200854],"study_design_scores_gemma":[0.000009539917,0.0001429877,0.000771988,0.000005098151,0.00001893842,0.0001108933,0.00001278835,0.9872113,0.01074893,0.0005303613,0.0004199051,0.00001721744],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05889112,0.0002153426,0.9380745,0.0001201377,0.00005920774,0.00004578091,0.000039593,0.001591927,0.0009624421],"genre_scores_gemma":[0.9459055,0.00009180838,0.05278959,0.000048685,0.0000387247,0.000030535,0.00003711687,0.00002595907,0.001032126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005081348,"threshold_uncertainty_score":0.01010358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01606393454178865,"score_gpt":0.2662423941316422,"score_spread":0.2501784595898536,"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."}}