{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001273573,0.00102739,0.0008944324,0.0009763267,0.0005109948,0.001430199,0.0009131592,0.001377842,0.003960844],"category_scores_gemma":[0.00457642,0.0005518071,0.001117414,0.002000731,0.00133268,0.003444842,0.001297216,0.001555401,0.001330165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001170973,"about_ca_system_score_gemma":0.001569234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00704302,"about_ca_topic_score_gemma":0.003763156,"domain_scores_codex":[0.9980935,0.0003171922,0.0001524136,0.0007375608,0.0005495129,0.0001498707],"domain_scores_gemma":[0.998652,0.000440243,0.0002681262,0.0001760693,0.0004224928,0.00004108324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001402718,0.00003557175,0.002186876,0.0005940301,0.000215399,0.0002056974,0.0002079433,0.2108677,0.004683922,0.5030094,0.006876556,0.2709766],"study_design_scores_gemma":[0.00003743075,0.0001279361,0.002353701,0.0001722856,0.0001608757,0.0003759207,0.0001203792,0.674702,0.006060885,0.2404591,0.07530683,0.00012262],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001175426,0.001934177,0.9910969,0.0002210001,0.000234479,0.00002407012,0.0001750706,0.000229076,0.004909778],"genre_scores_gemma":[0.4051361,0.01506528,0.5436862,0.001018512,0.001766247,0.0005442424,0.001558828,0.0002611359,0.03096348],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00704302,"threshold_uncertainty_score":0.01400405,"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."}}