{"id":"W3210294081","doi":"10.1145/3488281","title":"Wireless Localization with Spatial-Temporal Robust Fingerprints","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bell (Canada)","funders":"","keywords":"Computer science; RSS; Fingerprint (computing); Ambiguity; Multipath propagation; Software deployment; Key (lock); Wireless; Real-time computing; Fingerprint recognition; Representation (politics); Wireless sensor network; Artificial intelligence; Data mining; Telecommunications; Computer security; Computer network; World Wide Web","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":[],"consensus_categories":[],"category_scores_codex":[0.00004594869,0.0002124682,0.0002007,0.000102187,0.0001794348,0.00006122679,0.0001626597,0.000224066,0.0002452889],"category_scores_gemma":[0.000011571,0.0002063505,0.000063622,0.0005638164,0.00006138668,0.0001009989,0.000005090124,0.0003321956,0.00003533829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006933475,"about_ca_system_score_gemma":0.00002188792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000363124,"about_ca_topic_score_gemma":0.0003620807,"domain_scores_codex":[0.9990127,0.00003155475,0.0002263741,0.0002588285,0.0001793673,0.0002911754],"domain_scores_gemma":[0.9991651,0.00006863395,0.00003015221,0.0005593799,0.0001157363,0.00006097263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000178271,0.00003413736,0.0004389787,0.00001961387,0.00005261382,0.00003511938,0.00005486871,0.937506,0.00004866835,0.00003456908,0.0001554208,0.06160216],"study_design_scores_gemma":[0.0005398896,0.00005236578,0.000328186,0.00009966484,0.00005061582,0.00004427098,0.0003340831,0.9732226,0.02238184,0.00005800585,0.002505721,0.0003827607],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01139235,0.00008494144,0.9861318,0.0001323345,0.0004309506,0.000127139,0.000009125391,0.001115281,0.0005760352],"genre_scores_gemma":[0.9942477,0.0002858332,0.004874105,0.00013071,0.00006830267,0.00002426645,0.0000505944,0.00006511461,0.0002534073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9828553,"threshold_uncertainty_score":0.8414727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156374372646539,"score_gpt":0.1958265267320685,"score_spread":0.1842627830056031,"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."}}