{"id":"W4294024740","doi":"10.5194/tc-16-3489-2022","title":"Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles","year":2022,"lang":"en","type":"article","venue":"The cryosphere","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Ministère des Ressources naturelles et des Forêts; Université de Sherbrooke","funders":"Environment and Climate Change Canada; Hydro-Québec; Ministère des Forêts, de la Faune et des Parcs","keywords":"Data assimilation; Curse of dimensionality; Particle filter; State variable; Computer science; Classification of discontinuities; Forcing (mathematics); Filter (signal processing); Ensemble Kalman filter; Statistical physics; Mathematics; Meteorology; Kalman filter; Artificial intelligence; Physics; Extended Kalman filter","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0005619153,0.0004903231,0.0004925254,0.0003767756,0.0004060425,0.0005854241,0.0005331661,0.000774885,0.0006678667],"category_scores_gemma":[0.00127821,0.0003287848,0.0007877552,0.0005696003,0.0004056575,0.0007699578,0.0005874156,0.0008174322,0.0002183078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004002578,"about_ca_system_score_gemma":0.001205721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01456708,"about_ca_topic_score_gemma":0.01431859,"domain_scores_codex":[0.9998475,0.00003380006,0.00000914936,0.00003926519,0.00005253071,0.00001764243],"domain_scores_gemma":[0.9996628,0.0001310851,0.00003636005,0.00004489537,0.0001042935,0.00002063558],"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":[0.0001423564,0.00006619555,0.004390687,0.00009142246,0.0001316543,0.0001436955,0.000100539,0.8826357,0.0208003,0.00733958,0.002099198,0.08205863],"study_design_scores_gemma":[0.000004248691,0.000005304582,0.000310722,0.000002190274,0.000003738267,0.000006479901,0.000004522622,0.9979163,0.0008890174,0.0005174954,0.0003364853,0.000003484511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05267537,0.000270072,0.9453758,0.0002300195,0.0001027645,0.00002647014,0.0001012769,0.0003417368,0.0008764187],"genre_scores_gemma":[0.6164529,0.000465114,0.3799488,0.0001493186,0.0001164347,0.00007108834,0.0006387027,0.0001011172,0.00205656],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01456708,"threshold_uncertainty_score":0.02896458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04020731432220431,"score_gpt":0.2384676045776926,"score_spread":0.1982602902554883,"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."}}