{"id":"W4399841875","doi":"10.5194/hess-28-2617-2024","title":"Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts","year":2024,"lang":"en","type":"article","venue":"Hydrology and earth system sciences","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Horizon 2020","keywords":"Environmental science; Water content; Topographic Wetness Index; Soil science; Soil water; Hydrology (agriculture); Rut; Moisture; Remote sensing; Geology; Meteorology; Digital elevation model; Geotechnical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005263728,0.00009679744,0.0001224359,0.0002147962,0.0001405121,0.0001442174,0.00003916402,0.00006759425,5.853408e-7],"category_scores_gemma":[0.00001144734,0.00007308969,0.000008441893,0.0002596075,0.00007000763,0.0002626356,0.00002013255,0.00005758827,3.126272e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005160955,"about_ca_system_score_gemma":0.00001005337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002768188,"about_ca_topic_score_gemma":0.00161769,"domain_scores_codex":[0.9993028,0.00002337677,0.0001378579,0.0002397136,0.0001174472,0.0001788145],"domain_scores_gemma":[0.9998486,0.00003450303,0.00001648295,0.00003713002,0.00001546542,0.00004782879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001068224,0.00001703579,0.739339,0.004031233,0.0001755339,0.00003770395,0.001815063,0.2464513,0.001516688,0.004550642,0.00003618909,0.001922759],"study_design_scores_gemma":[0.0002995882,0.00008852606,0.01069732,0.0002676652,0.00002176704,0.00002831747,0.0001614339,0.9880828,0.00007233873,0.00003605212,0.0001492278,0.00009501306],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919084,0.005380863,0.00174996,0.00005576995,0.0001513149,0.000254208,0.000004957617,0.0001025344,0.0003920465],"genre_scores_gemma":[0.9995884,0.0002259674,0.0001066362,0.00001972423,0.00001992179,0.00001613645,0.000002917475,0.000006134477,0.00001416982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7416314,"threshold_uncertainty_score":0.2980511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03140705472136261,"score_gpt":0.224408222319995,"score_spread":0.1930011675986324,"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."}}