{"id":"W2318814119","doi":"10.2514/6.2013-4767","title":"Kriged Kalman Filtering for Predicting the Spatio-Temporal Wildfire Temperature Process Evolution","year":2013,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control (GNC) Conference","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kalman filter; Process (computing); Computer science; Remote sensing; Environmental science; Artificial intelligence; Geology","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":[0.001000056,0.0004922532,0.0005717894,0.0003492326,0.0003085619,0.0006002788,0.0007123078,0.0007656245,0.0005936244],"category_scores_gemma":[0.002613499,0.0004587576,0.0005880733,0.0005406868,0.0005427341,0.001185197,0.0005115014,0.001128005,0.0001555835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007930407,"about_ca_system_score_gemma":0.00120704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01941497,"about_ca_topic_score_gemma":0.01525978,"domain_scores_codex":[0.9997494,0.00005763502,0.00001795889,0.00008134052,0.00006175981,0.00003190086],"domain_scores_gemma":[0.999527,0.0002597064,0.00008145563,0.00004231762,0.00007552116,0.00001399811],"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":[0.00001651941,0.000008593515,0.0007099682,0.00001930074,0.00001818491,0.00001911915,0.00002437828,0.9776371,0.0009104938,0.01014277,0.0001521386,0.0103414],"study_design_scores_gemma":[0.000001598897,0.000002931183,0.0001164527,0.000001486745,0.000002582238,0.000002927155,0.000002136031,0.9976971,0.0001506111,0.001912405,0.0001072182,0.000002594944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02355407,0.0001480716,0.9752508,0.0001003353,0.00002815614,0.00001105638,0.00006651398,0.0001794457,0.0006615839],"genre_scores_gemma":[0.8588059,0.0006859339,0.1375235,0.00007064478,0.00005585446,0.00007114787,0.0002890044,0.00005927051,0.002438737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01941497,"threshold_uncertainty_score":0.0386039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006643703563006674,"score_gpt":0.2214651479759647,"score_spread":0.214821444412958,"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."}}