{"id":"W4220835992","doi":"10.5194/egusphere-egu22-2084","title":"Assessment of the impacts on data assimilation performance caused by spatio-temporal gaps in satellite soil moisture data","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Data assimilation; Environmental science; Water content; Satellite; Forcing (mathematics); Terrain; Remote sensing; Moisture; Watershed; Vegetation (pathology); Meteorology; Atmospheric sciences; Computer science; Geography; Geology; Cartography","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":[0.004177078,0.0005178099,0.0003625249,0.0004051192,0.0005298943,0.0008630711,0.0005081877,0.00107924,0.0006079342],"category_scores_gemma":[0.01613256,0.0002915217,0.0005850022,0.0006007897,0.0005410384,0.001071313,0.0008489127,0.000718801,0.0001192242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006368785,"about_ca_system_score_gemma":0.0005490714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01268921,"about_ca_topic_score_gemma":0.006627807,"domain_scores_codex":[0.9987934,0.0003697578,0.0001596271,0.0002163333,0.0003012015,0.000159568],"domain_scores_gemma":[0.9876573,0.008145743,0.00134063,0.0008492416,0.001687515,0.0003194819],"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":[0.003446847,0.0007567828,0.2069963,0.0003442419,0.0004307594,0.001451176,0.0005082834,0.6970977,0.0372716,0.001084156,0.0009987361,0.04961358],"study_design_scores_gemma":[0.0001306667,0.001401728,0.2021226,0.00007988453,0.000169479,0.0002949051,0.0006005847,0.7597091,0.03396872,0.0005557686,0.0008837906,0.00008267209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995234,0.0001667536,0.003377141,0.0001439967,0.00003488216,0.00003010465,0.0002944298,0.00009176789,0.0006270605],"genre_scores_gemma":[0.9971035,0.00006353055,0.002211306,0.00003613591,0.000007990458,0.00002341611,0.0003641172,0.00001709918,0.000172903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01268921,"threshold_uncertainty_score":0.02523071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0372633596398447,"score_gpt":0.2994529647409003,"score_spread":0.2621896051010556,"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."}}