{"id":"W4315700433","doi":"10.3390/w15020321","title":"Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation","year":2023,"lang":"en","type":"article","venue":"Water","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Hydro-Québec","keywords":"Environmental science; Hydrometeorology; Water content; Streamflow; Data assimilation; Satellite; Classification of discontinuities; Data set; Remote sensing; Soil science; Meteorology; Geology; Precipitation; Computer science; Drainage basin; Geography; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003182544,0.0004300546,0.0004140445,0.0002133602,0.0005058313,0.0006793501,0.0004717132,0.0006367969,0.0003902758],"category_scores_gemma":[0.01184723,0.0002590113,0.0004475419,0.0005992959,0.0006313779,0.0011874,0.0006395652,0.0007207233,0.00006140681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005685298,"about_ca_system_score_gemma":0.000633907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01029563,"about_ca_topic_score_gemma":0.006602508,"domain_scores_codex":[0.9984593,0.0004326224,0.0002756524,0.0003300556,0.0003588276,0.0001434456],"domain_scores_gemma":[0.990265,0.006295551,0.001046898,0.00139777,0.0007631286,0.0002317063],"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":[0.00201344,0.0008238427,0.1477302,0.0002751634,0.000350878,0.0007722833,0.0004382396,0.7694138,0.04175581,0.001439653,0.0009027395,0.03408389],"study_design_scores_gemma":[0.0001903563,0.001279458,0.1434519,0.00006808452,0.0001592689,0.0003217124,0.0008947456,0.7942736,0.05574415,0.001198552,0.002346914,0.00007125146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995176,0.0001150544,0.003685771,0.000129927,0.00003719076,0.00001662455,0.0003173191,0.00008149661,0.0004407541],"genre_scores_gemma":[0.9978722,0.00004506443,0.001600049,0.00003402064,0.000005861867,0.00001366999,0.0003665844,0.00001090036,0.00005177613],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01029563,"threshold_uncertainty_score":0.02047139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04727629080664872,"score_gpt":0.2826055782460206,"score_spread":0.2353292874393718,"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."}}