{"id":"W2007673730","doi":"10.3997/1873-0604.2014003","title":"Integrating MRS data with hydrologic model ‐ Carrizal Catchment (Spain)","year":2014,"lang":"en","type":"article","venue":"Near Surface Geophysics","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vanguard College","funders":"","keywords":"Aquifer; Hydraulic conductivity; Hydrogeology; Geology; MODFLOW; Hydrology (agriculture); Specific storage; Hydrological modelling; Groundwater model; Hydraulic head; Soil science; Groundwater; Groundwater flow; Geotechnical engineering; Groundwater recharge","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":[0.0001369017,0.0002054948,0.0002048375,0.000006208899,0.000302543,0.0001017833,0.0004880042,0.00003253571,0.00004226045],"category_scores_gemma":[0.000002463244,0.0001671973,0.00004217502,0.0001595788,0.00009026695,0.0001904579,0.0001779139,0.0002589159,0.0001345881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001828694,"about_ca_system_score_gemma":0.00009725485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004974115,"about_ca_topic_score_gemma":0.00001653611,"domain_scores_codex":[0.9988523,0.00003580072,0.0001581173,0.0004400745,0.0001818569,0.000331835],"domain_scores_gemma":[0.9987223,0.00005359651,0.0001073329,0.0009822503,0.0000446285,0.00008990807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000722249,0.0008215586,0.06337193,0.00003799284,0.0002902669,0.000001546892,0.001627185,0.342083,0.02331051,0.5465397,0.003244017,0.01860009],"study_design_scores_gemma":[0.0003433324,0.0000922876,0.0002175866,0.00001506227,0.00005180751,2.867708e-7,0.0002281084,0.978983,0.002515163,0.0131827,0.004064207,0.0003064128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8200044,0.000007652708,0.1675259,0.0003211359,0.00003616268,0.0001977782,0.0001213783,0.00007005341,0.01171548],"genre_scores_gemma":[0.9648939,8.732342e-7,0.03403771,0.0001124199,0.0001820511,0.00001741775,0.0002961329,0.00002661497,0.00043287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6369001,"threshold_uncertainty_score":0.6818107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01458059777515574,"score_gpt":0.2955099959061166,"score_spread":0.2809293981309609,"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."}}