{"id":"W2117373029","doi":"10.1145/2480730.2480731","title":"System-level calibration for data fusion in wireless sensor networks","year":2013,"lang":"en","type":"article","venue":"ACM Transactions on Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Office of Integrative Activities; Division of Electrical, Communications and Cyber Systems; Division of Computer and Network Systems; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Wireless sensor network; Real-time computing; Calibration; Overhead (engineering); Testbed; Sensor fusion; Noise (video); False alarm; Artificial intelligence; Computer network","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":[0.003695919,0.001049141,0.001051161,0.0007511117,0.000818942,0.001140516,0.001638932,0.001157437,0.0009477261],"category_scores_gemma":[0.01372006,0.0006564769,0.0007220075,0.001197936,0.001288435,0.003073512,0.00249136,0.001906374,0.0003574883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239677,"about_ca_system_score_gemma":0.001246628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482243,"about_ca_topic_score_gemma":0.001285901,"domain_scores_codex":[0.996937,0.001129926,0.0001385128,0.0006137064,0.001003504,0.0001773336],"domain_scores_gemma":[0.9967673,0.001512223,0.0003231466,0.0008069284,0.0005269917,0.00006333349],"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":[0.00008888331,0.00006985738,0.001187053,0.0001434,0.00006909958,0.00006004414,0.000162955,0.8450916,0.009537848,0.01921708,0.001079996,0.1232923],"study_design_scores_gemma":[0.00000622345,0.0000351306,0.0002068974,0.000009578752,0.000008061736,0.00003668425,0.00001856238,0.9845983,0.004795487,0.009329031,0.0009430262,0.00001301313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003482575,0.0001662225,0.9956083,0.00008094159,0.00001577686,0.00001971386,0.00001005618,0.0002788673,0.0003375894],"genre_scores_gemma":[0.6917621,0.0005271123,0.3062459,0.0002038412,0.00007570555,0.0001843749,0.0001221436,0.0001457591,0.0007331062],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003695919,"threshold_uncertainty_score":0.01954615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03199152621096626,"score_gpt":0.234507044019837,"score_spread":0.2025155178088708,"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."}}