{"id":"W4408278545","doi":"10.1016/j.jhydrol.2025.133059","title":"Estimating soil hydraulic conductivity from time-lapse ground-penetrating radar data in podzolic soils using the green-ampt model","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Newfoundland and Labrador; Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland; Research and Development Corporation of Newfoundland and Labrador","keywords":"Soil water; Hydraulic conductivity; Geology; Soil science; Ground-penetrating radar; Hydrology (agriculture); Environmental science; Radar; Geotechnical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000698026,0.0001525797,0.0003784578,0.0001138715,0.000111378,0.00003771287,0.0006065947,0.00009825561,0.000009513907],"category_scores_gemma":[0.0001337931,0.0001207585,0.00006506698,0.0002731403,0.00007529655,0.0002929627,0.0001613984,0.0006206268,0.000004541655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005762482,"about_ca_system_score_gemma":0.00008010491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006532725,"about_ca_topic_score_gemma":0.00009895183,"domain_scores_codex":[0.9987379,0.0001533402,0.0005370806,0.0001844106,0.0001308384,0.0002563927],"domain_scores_gemma":[0.9987004,0.0005267491,0.0001920421,0.0004923034,0.00003794156,0.00005052388],"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":[0.00001194964,0.00006702016,0.0002223541,0.00002477194,0.00009875344,0.0000109132,0.0002261089,0.6719519,0.3180399,0.0002920602,0.00009106711,0.00896317],"study_design_scores_gemma":[0.0003200683,0.00001700585,0.001324229,0.00004816617,0.00006129126,0.00002949274,0.00002508965,0.9639059,0.0004582932,0.03366141,0.00005388501,0.00009520726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918489,0.0002686648,0.1064909,0.0008347587,0.0001768765,0.00008435473,0.0000158655,0.00002219518,0.0002575084],"genre_scores_gemma":[0.913103,0.000007530869,0.08638339,0.0002181434,0.0002462949,0.000003128236,0.000006820097,0.00001645506,0.00001523721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3175816,"threshold_uncertainty_score":0.4924389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04221223608949761,"score_gpt":0.3129555728635829,"score_spread":0.2707433367740854,"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."}}