{"id":"W2052361115","doi":"10.1109/icosp.2014.7015323","title":"Spatial Kalman Filters and Spatial-Temporal Kalman Filters","year":2014,"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":"","funders":"National Natural Science Foundation of China; Canada Millennium Scholarship Foundation","keywords":"Kalman filter; Fast Kalman filter; Computer science; Computation; Spatial analysis; Spatial filter; Extended Kalman filter; Artificial intelligence; Algorithm; Geography; Remote sensing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003723802,0.0002478611,0.0002545253,0.0001167047,0.0002398175,0.000307024,0.0007958959,0.0001143708,0.0002036879],"category_scores_gemma":[0.00005201547,0.0002136572,0.00007162041,0.0001738427,0.0001047396,0.000377854,0.0004685411,0.0002105414,0.0001222354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001576276,"about_ca_system_score_gemma":0.00002054629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003296156,"about_ca_topic_score_gemma":0.0008239904,"domain_scores_codex":[0.9980636,0.0001342933,0.0003289313,0.000671825,0.0003468744,0.0004544342],"domain_scores_gemma":[0.9985173,0.0001899888,0.0001091308,0.0008769544,0.00004951159,0.0002570692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004203114,0.0001401188,0.0318313,0.00004815566,0.00004719643,0.00005100336,0.0009393392,0.0006168302,0.0005374608,0.02023525,0.1451871,0.8003241],"study_design_scores_gemma":[0.001252033,0.0003957085,0.03177213,0.00006539834,0.00001719931,0.00006598319,0.00004321054,0.7523132,0.001297271,0.002352031,0.2095455,0.0008803796],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02856056,0.00004026561,0.9606012,0.001460677,0.001355859,0.0001427804,0.000008425111,0.0004620177,0.007368227],"genre_scores_gemma":[0.9615934,0.00001717287,0.03581876,0.001402807,0.0003746647,0.000006607851,0.00003173276,0.00001820532,0.0007366209],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9330329,"threshold_uncertainty_score":0.8712687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01204326814065748,"score_gpt":0.2218870245429614,"score_spread":0.2098437564023039,"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."}}