{"id":"W3014715826","doi":"","title":"Towards Improved Subsurface Representation in Land Surface-Hydrology Models","year":2018,"lang":"en","type":"article","venue":"AGU Fall Meeting 2018","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Global Institute for Water Security","funders":"","keywords":"Hydrology (agriculture); Environmental science; Subsurface flow; Surface water; Representation (politics); Groundwater; Geology; Environmental engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007573395,0.0005642413,0.0007095835,0.0003961994,0.0002404223,0.001199177,0.001017711,0.001006452,0.001562012],"category_scores_gemma":[0.003691646,0.0005990372,0.0006844925,0.000557352,0.0002675106,0.001874162,0.001069369,0.001501287,0.0006850874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004969413,"about_ca_system_score_gemma":0.0009676315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01194599,"about_ca_topic_score_gemma":0.0105824,"domain_scores_codex":[0.9997881,0.0000907874,0.00001668186,0.00003187114,0.00005254492,0.00002001231],"domain_scores_gemma":[0.9992301,0.0003049303,0.0000547062,0.000162901,0.000209882,0.00003746233],"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":[0.00003192313,0.00004608071,0.0007251924,0.00002047805,0.00002177654,0.00001735356,0.00002325518,0.9750341,0.002722235,0.003475473,0.0005854688,0.01729667],"study_design_scores_gemma":[0.000002109284,0.00000279784,0.000021045,8.200468e-7,0.000001434591,8.797516e-7,0.00000177695,0.9990394,0.0001995897,0.0006108662,0.0001185706,7.768565e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06201823,0.0001149128,0.9337093,0.0003104093,0.00005419997,0.00004180643,0.0004933535,0.001797374,0.001460442],"genre_scores_gemma":[0.5990506,0.0002467849,0.3962881,0.0001579925,0.0000599435,0.0001314243,0.001508307,0.0005153818,0.002041553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01194599,"threshold_uncertainty_score":0.02375293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02127933702596128,"score_gpt":0.2534755688465363,"score_spread":0.232196231820575,"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."}}