{"id":"W3133006395","doi":"10.1016/j.jhydrol.2021.126132","title":"Quantifying the effect of surface heterogeneity on soil moisture across regions and surface characteristic","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Environmental science; Spatial heterogeneity; Digital elevation model; Elevation (ballistics); Surface runoff; Land cover; Mean squared error; Scale (ratio); Topographic Wetness Index; Vegetation (pathology); Advanced Spaceborne Thermal Emission and Reflection Radiometer; Hydrology (agriculture); Soil science; Spatial distribution; Remote sensing; Geology; Land use; Mathematics; Statistics; Geography; Cartography; Geometry","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":[0.0005495365,0.0002008582,0.0002599551,0.0005185313,0.0001489582,0.0005143718,0.0002381853,0.0003363693,0.0004054414],"category_scores_gemma":[0.002242456,0.0001951416,0.0003176199,0.0006638741,0.0003377357,0.0007855335,0.0003113009,0.0001816133,0.00005993301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003385209,"about_ca_system_score_gemma":0.0002244964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008631568,"about_ca_topic_score_gemma":0.01278588,"domain_scores_codex":[0.9997436,0.00005232507,0.00001256372,0.0001005712,0.0000436758,0.00004730721],"domain_scores_gemma":[0.9982482,0.001206376,0.0002088469,0.0001797787,0.00009030709,0.00006643832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005567118,0.0001362115,0.7312722,0.00007112663,0.0004985684,0.0001656503,0.00009966529,0.1719968,0.06704732,0.0004444652,0.000167942,0.02754333],"study_design_scores_gemma":[0.00001806547,0.00009778497,0.8143536,0.000004595363,0.0001426943,0.00007688336,0.000105458,0.1774698,0.00711842,0.0004151695,0.0001765265,0.00002113357],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973118,0.00003033607,0.002243567,0.00000786189,0.00000132132,0.000003516732,0.000139084,0.00002285563,0.0002397053],"genre_scores_gemma":[0.9995122,0.000008547237,0.0003757069,0.00000261016,0.000001295654,8.810111e-7,0.00007258883,0.000003883113,0.00002230183],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008631568,"threshold_uncertainty_score":0.01716262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01625014173725287,"score_gpt":0.278497846014957,"score_spread":0.2622477042777042,"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."}}