{"id":"W4412373162","doi":"10.22541/essoar.175242054.44618654/v1","title":"Characterizing Spatial Heterogeneity of Hydraulic Conductivity using Borehole NMR in a Complex Groundwater Flow System","year":2025,"lang":"en","type":"preprint","venue":"","topic":"NMR spectroscopy and applications","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council; University of Waterloo","keywords":"Hydraulic conductivity; Borehole; Groundwater flow; Groundwater; Geology; Flow (mathematics); Groundwater model; Soil science; Spatial variability; Hydrology (agriculture); Environmental science; Geotechnical engineering; Petroleum engineering; Aquifer; Mechanics; Soil water; Physics; Mathematics; Statistics","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":[0.0003586436,0.0003253015,0.0002043391,0.0004152718,0.0002683618,0.0004246469,0.0004066588,0.0003991296,0.0001610034],"category_scores_gemma":[0.0009399116,0.0001963288,0.0002649222,0.0003950689,0.0004997527,0.00083189,0.000415509,0.0002080836,0.00002701151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006141731,"about_ca_system_score_gemma":0.0005027736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01251224,"about_ca_topic_score_gemma":0.01191863,"domain_scores_codex":[0.9998493,0.00002966352,0.00001145671,0.00005765865,0.00002437875,0.00002747533],"domain_scores_gemma":[0.9996443,0.0001606592,0.00007718244,0.00004046075,0.00005806893,0.00001928121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002182397,0.000150113,0.08195435,0.00005843485,0.00005019163,0.000325079,0.0003057101,0.8026723,0.09153403,0.001072899,0.0001663408,0.02149232],"study_design_scores_gemma":[0.00001520645,0.0001152719,0.04008346,0.000004383979,0.00002355122,0.00005062532,0.0001502497,0.9500383,0.008680549,0.0006671053,0.0001370975,0.00003418001],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9856805,0.00001951426,0.0139106,0.00002442812,0.000002012228,0.000008609937,0.00006111164,0.00009468831,0.0001985387],"genre_scores_gemma":[0.998334,0.0000106971,0.001585219,0.000003430972,7.120121e-7,0.000003561643,0.00002763451,0.000002466043,0.00003237158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01251224,"threshold_uncertainty_score":0.0248788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04450400053563656,"score_gpt":0.3420272029095392,"score_spread":0.2975232023739026,"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."}}