{"id":"W2376914131","doi":"","title":"Investigation on Localization and Tracking of Moving Target in WSN","year":2009,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wireless sensor network; Computer science; Tracking (education); Wireless; Nonlinear system; Real-time computing; Variable (mathematics); Match moving; Key distribution in wireless sensor networks; Extended Kalman filter; Kalman filter; Artificial intelligence; Motion (physics); Wireless network; Telecommunications; Computer network; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000634692,0.0002663125,0.0002910741,0.0004327618,0.0002668888,0.000495737,0.0003346625,0.0005019511,0.0008011993],"category_scores_gemma":[0.00203707,0.0001872872,0.0003415958,0.0007818278,0.0004006741,0.001691703,0.0002909387,0.0003059784,0.0001635832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004188773,"about_ca_system_score_gemma":0.0003038696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002243915,"about_ca_topic_score_gemma":0.0009994064,"domain_scores_codex":[0.9996027,0.00008122728,0.0000212942,0.00008975637,0.0001700514,0.00003498183],"domain_scores_gemma":[0.9995672,0.000189329,0.00004970739,0.00002494382,0.0001563965,0.00001240872],"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":[0.0001608464,0.00006469383,0.01258033,0.001229293,0.0001254111,0.001243149,0.0007687335,0.5151379,0.05993647,0.1244768,0.002585009,0.2816915],"study_design_scores_gemma":[0.000009982531,0.0001815134,0.004141294,0.00008517109,0.00004584499,0.0008496823,0.0002859533,0.9532001,0.01199033,0.01662814,0.0125554,0.00002668042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08745121,0.00785602,0.8885278,0.000736051,0.0001347314,0.00004269448,0.00003845731,0.0001814186,0.01503162],"genre_scores_gemma":[0.9166337,0.01276646,0.06213406,0.0001787378,0.0001358978,0.00005532181,0.00009774799,0.00003890617,0.007959141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002243915,"threshold_uncertainty_score":0.004461765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092066418148138,"score_gpt":0.2038108434519355,"score_spread":0.1928901792704541,"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."}}