{"id":"W2849540937","doi":"10.18280/mmep.050206","title":"Investigation of wireless tracking performance in the tunnel-like environment with particle filter","year":2018,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tracking (education); Particle filter; Wireless; Particle (ecology); Computer science; Acoustics; Environmental science; Filter (signal processing); Aerospace engineering; Marine engineering; Engineering; Telecommunications; Electrical engineering; Physics; Geology; Psychology","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":[0.001162134,0.0006572069,0.0007221675,0.0006285755,0.0004289641,0.0006544605,0.0005824474,0.00106318,0.0006142869],"category_scores_gemma":[0.003086888,0.0002269252,0.000642113,0.0007773541,0.0004080058,0.0009460281,0.0006013362,0.0006599491,0.0001856138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003482429,"about_ca_system_score_gemma":0.0005762901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008796101,"about_ca_topic_score_gemma":0.003163241,"domain_scores_codex":[0.9995316,0.0001239778,0.00003166214,0.00008555791,0.0001250007,0.0001021493],"domain_scores_gemma":[0.9985128,0.0007396865,0.0001678659,0.0001489773,0.0003658986,0.00006479784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002568251,0.0001460839,0.01130944,0.0001946948,0.00009166627,0.0004529398,0.0002015496,0.9439371,0.008077051,0.002207943,0.0005642155,0.03256039],"study_design_scores_gemma":[0.000009081391,0.0001870917,0.002955655,0.00001166662,0.00001738083,0.00008660868,0.00006823233,0.9934794,0.002529127,0.0003958839,0.0002434262,0.00001639645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7318537,0.0007451142,0.2600754,0.0003949822,0.0001455614,0.0000755773,0.0002040831,0.0008793586,0.005626169],"genre_scores_gemma":[0.983458,0.00025997,0.01522976,0.00002864123,0.000008361933,0.00002847254,0.0001458441,0.00001900805,0.0008218975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008796101,"threshold_uncertainty_score":0.01748979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324249809073182,"score_gpt":0.173240158897213,"score_spread":0.1499976608064812,"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."}}