{"id":"W2099684071","doi":"10.1109/icc.2007.547","title":"A2L: Angle to Landmarks Based Method Positioning for Wireless Sensor Networks","year":2007,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Wireless sensor network; Computer science; Landmark; Angle of arrival; Node (physics); Key distribution in wireless sensor networks; Wireless; Real-time computing; Wireless network; Computer network; Artificial intelligence; Telecommunications; Engineering","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":[0.0002777711,0.000104676,0.0001199412,0.0001206502,0.0000727571,0.00002979971,0.00008758992,0.000130652,0.00004957681],"category_scores_gemma":[0.00002809943,0.00009906765,0.00004805165,0.0002379109,0.000008298653,0.00004043862,0.0000125412,0.00007592071,0.00000794827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004559905,"about_ca_system_score_gemma":0.000004358091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001679954,"about_ca_topic_score_gemma":0.00006259317,"domain_scores_codex":[0.9993616,0.000007068751,0.0001568578,0.0001206135,0.00006530404,0.0002885895],"domain_scores_gemma":[0.9995747,0.0001683635,0.00001174119,0.0001366307,0.00005515766,0.00005341403],"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":[0.00004272349,0.00001461825,0.001135782,0.00004685288,0.00002782559,0.000006123313,0.00006879007,0.9392295,0.004004891,0.005914765,0.007439407,0.04206873],"study_design_scores_gemma":[0.0003454321,0.00004087008,0.0003170955,0.00001842513,0.00001039199,0.000002584464,0.0001667727,0.8876545,0.1051529,0.00007893627,0.006011077,0.0002009771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008042277,0.00002236191,0.9859049,0.0001054909,0.0002071891,0.000234486,0.000004350596,0.001177465,0.004301445],"genre_scores_gemma":[0.7349209,0.000001942814,0.2642508,0.0004246312,0.00009101121,0.00002462726,0.00003009523,0.00003309155,0.0002228642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7268786,"threshold_uncertainty_score":0.4039862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007956558705085713,"score_gpt":0.2556433683378355,"score_spread":0.2476868096327498,"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."}}