{"id":"W2083056209","doi":"10.1109/glocom.2013.6831121","title":"Selectively iterative particle filtering and its applications for target tracking in WSNs","year":2013,"lang":"en","type":"article","venue":"","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; University of Waterloo","funders":"","keywords":"Particle filter; Tracking (education); Degeneracy (biology); Wireless sensor network; Computer science; Divergence (linguistics); Kalman filter; Algorithm; Gaussian; Particle (ecology); Artificial intelligence; Physics","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":[0.0001280078,0.00008956227,0.0001032769,0.00004468743,0.0001330166,0.0002427006,0.0002273348,0.00003784809,0.00003846548],"category_scores_gemma":[0.00003601135,0.00008024126,0.00001920026,0.0002529074,0.00001328177,0.0007740479,0.00009347589,0.00008030568,0.00002598921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001484009,"about_ca_system_score_gemma":0.00001156028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001895071,"about_ca_topic_score_gemma":0.00001017546,"domain_scores_codex":[0.9991564,0.00002680155,0.0001747403,0.0003186339,0.00007144255,0.0002520217],"domain_scores_gemma":[0.9993883,0.0002465385,0.00003615768,0.0001662911,0.00009310222,0.0000696394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002570637,0.000510896,0.01437183,0.0001242826,0.00005410914,0.000006560558,0.009512082,0.006996189,0.09150412,0.5836821,0.008073695,0.2851385],"study_design_scores_gemma":[0.0004298213,0.00005833471,0.01133902,0.00001989671,0.000001894466,0.000007967433,0.0000907623,0.9482571,0.0238978,0.009669841,0.006002052,0.000225449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05531544,0.0001257133,0.9426385,0.0005991246,0.00005122767,0.0006110372,0.000006192441,0.0001297673,0.0005229798],"genre_scores_gemma":[0.8677502,0.000009427245,0.1314231,0.0002093733,0.00004220898,0.0003852936,0.000004032715,0.000006243275,0.0001700593],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.941261,"threshold_uncertainty_score":0.3272144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02286502005398726,"score_gpt":0.2669855074892467,"score_spread":0.2441204874352594,"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."}}