{"id":"W2145196717","doi":"10.1109/ispa.2007.4383744","title":"Distributed Online Self-Localization and Tracking in Sensor Networks","year":2007,"lang":"en","type":"article","venue":"International symposium on image and signal processing and analysis/ISPA ...","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Belief propagation; Graphical model; Expectation–maximization algorithm; Computer science; Wireless sensor network; Gaussian; Factor graph; Maximization; Graph; Maximum likelihood; Algorithm; Extension (predicate logic); Tracking (education); Graph theory; Mathematical optimization; Theoretical computer science; Artificial intelligence; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009796564,0.0004142849,0.0007950959,0.0004110648,0.0003796089,0.0007546498,0.001051994,0.0009310166,0.0009635859],"category_scores_gemma":[0.003173957,0.0003460096,0.0003280359,0.0007376022,0.000764732,0.001616495,0.001253767,0.0006144904,0.0003298309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006352957,"about_ca_system_score_gemma":0.0006336536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002168477,"about_ca_topic_score_gemma":0.001914181,"domain_scores_codex":[0.9992939,0.0002374709,0.00002700975,0.0001850378,0.0001975952,0.00005882506],"domain_scores_gemma":[0.9989426,0.0005823213,0.0001025729,0.000189609,0.0001467643,0.00003628406],"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":[0.0001010614,0.00003870553,0.0005330023,0.00009101011,0.00003484639,0.00008899075,0.00009056007,0.8202641,0.003706801,0.034748,0.00188662,0.1384163],"study_design_scores_gemma":[0.000005880963,0.00001159566,0.00007683755,0.000002537145,0.000002429643,0.00001828672,0.000005371658,0.9864878,0.0006126654,0.01199604,0.000776737,0.000003650842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007379957,0.0003782185,0.990719,0.0001279123,0.00002858276,0.00001181827,0.0000250763,0.0004570482,0.0008725097],"genre_scores_gemma":[0.7770232,0.0009216274,0.2153569,0.0001312925,0.00010346,0.0001575244,0.0001767974,0.0001177699,0.006011399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002168477,"threshold_uncertainty_score":0.005181015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008572751754202242,"score_gpt":0.262323546187283,"score_spread":0.2537507944330808,"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."}}