{"id":"W2020181805","doi":"10.1109/icc.2013.6654726","title":"Robust wireless multihop localization using mobile anchors","year":2013,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Node (physics); Key distribution in wireless sensor networks; Global Positioning System; Kalman filter; Computer network; Sensor node; Scheme (mathematics); Wireless; Position (finance); Real-time computing; Mobile wireless sensor network; Wireless network; Engineering; Artificial intelligence; Telecommunications; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003026587,0.0001194208,0.0001082868,0.0001014234,0.00006569738,0.00005245651,0.0001093992,0.0001246263,0.000527374],"category_scores_gemma":[0.00001002622,0.0001090755,0.00002852339,0.0002784232,0.00003720097,0.0002460777,0.00002445161,0.00007441507,0.0001722575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005833173,"about_ca_system_score_gemma":0.000005629227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001094509,"about_ca_topic_score_gemma":0.000009372092,"domain_scores_codex":[0.9994029,0.000006913146,0.0001726849,0.0001175701,0.00009301602,0.0002068897],"domain_scores_gemma":[0.999693,0.00001173778,0.00001592453,0.0001744617,0.00006887916,0.00003597422],"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":[3.714919e-7,0.000008893079,0.001721374,0.00003143362,0.000009247035,6.634967e-7,0.00007417476,0.9805748,0.003205804,0.00051723,0.002765168,0.01109078],"study_design_scores_gemma":[0.0001216629,0.000007884595,0.0001295296,0.00001160173,0.000004154687,0.000001951313,0.0003346813,0.9539642,0.0439474,0.000122597,0.001198592,0.0001557771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2651566,0.0000851261,0.7304525,0.000007535333,0.0002077512,0.0002452938,0.000001226595,0.001508149,0.002335837],"genre_scores_gemma":[0.9954543,0.00004761905,0.00414945,0.00006844806,0.00003583797,0.00004692638,0.000009413547,0.00003264279,0.0001553925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7302977,"threshold_uncertainty_score":0.5774375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01718446597212997,"score_gpt":0.2056252610543491,"score_spread":0.1884407950822191,"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."}}