{"id":"W2917589794","doi":"10.1175/jhm-d-18-0140.1","title":"Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Ministère des Ressources naturelles et des Forêts; Université de Sherbrooke","funders":"Hydro-Québec","keywords":"Snow; Environmental science; Spatial coherence; Multivariate interpolation; Water equivalent; Snowpack; Spatial distribution; Particle (ecology); Interpolation (computer graphics); Ensemble Kalman filter; Meteorology; Atmospheric sciences; Mathematics; Coherence (philosophical gambling strategy); Statistics; Physics; Geology; Kalman filter; Bilinear interpolation; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003612105,0.00005849632,0.00015378,0.000009558995,0.000099224,0.00001194339,0.0001993519,0.00002485258,0.001058241],"category_scores_gemma":[0.00005490922,0.00002407827,0.00008920316,0.0001204235,0.00006020992,0.00006247004,0.00003696703,0.0000949895,0.00003705492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006546437,"about_ca_system_score_gemma":0.00001856375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004370711,"about_ca_topic_score_gemma":0.0003692261,"domain_scores_codex":[0.99923,0.0000779328,0.0002745189,0.00006291458,0.0001683795,0.0001863006],"domain_scores_gemma":[0.9995304,0.00009020336,0.0001430558,0.0001324313,0.00006589371,0.00003797496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000117866,0.00002433184,0.8844035,0.000008335966,0.00007197102,0.000003920907,0.0005454529,0.1057144,0.006125144,0.00004163491,0.0005198985,0.002423533],"study_design_scores_gemma":[0.0002254996,0.0003727929,0.9719303,0.000009586798,0.00004365712,0.00005951695,0.0000697608,0.02042582,0.001802433,0.0002969628,0.004719573,0.00004414053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994953,0.0001088368,0.0008221684,0.003173204,0.0007824969,0.00009911638,0.00001617625,0.000001606434,0.00004338248],"genre_scores_gemma":[0.999416,0.000005646708,0.0001935743,0.0002540076,0.00007266842,1.888296e-7,0.000002343674,0.000001477993,0.00005411223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08752678,"threshold_uncertainty_score":0.9998549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011993772127534,"score_gpt":0.2761729693544115,"score_spread":0.2460530316331362,"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."}}