{"id":"W2027450714","doi":"10.1109/icgpr.2010.5550182","title":"GPR, ERT and CPT data integration for high resolution aquifer modeling","year":2010,"lang":"en","type":"article","venue":"","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrogeology; Electrical resistivity tomography; Ground-penetrating radar; Aquifer; Geology; Groundwater; Groundwater flow; Soil science; Hydraulic conductivity; Cone penetration test; Vadose zone; Watershed; Hydrology (agriculture); Geotechnical engineering; Electrical resistivity and conductivity; Soil water; 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":[0.0000966747,0.00004901953,0.0000524654,0.00001406924,0.00004249001,0.00001643399,0.0000796298,0.00003890074,0.000009729446],"category_scores_gemma":[0.00003466306,0.00004095459,0.000008087352,0.00003882985,0.000007994409,0.0001137186,0.00002689472,0.00007992878,0.000005395834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003049365,"about_ca_system_score_gemma":0.000002359695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006321372,"about_ca_topic_score_gemma":0.00008263149,"domain_scores_codex":[0.9997003,0.000003069231,0.00007664356,0.0001147266,0.00003075123,0.00007451703],"domain_scores_gemma":[0.9996518,0.00003965094,0.000005367712,0.0002535272,0.00002154136,0.00002809042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005821451,0.0000281723,0.0000103686,0.0000318216,0.00001423211,4.239898e-8,0.00005513345,0.008774083,0.5682801,0.2708167,0.001858952,0.1501246],"study_design_scores_gemma":[0.00006914373,0.000005216991,0.0002070887,0.0000024871,0.000007167001,2.809059e-7,0.00001216813,0.9770304,0.001364108,0.02003333,0.001212981,0.00005560487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.144444,0.00000872788,0.8544522,0.0002185388,0.00009453519,0.0001192534,0.00001961641,0.0000965195,0.0005466003],"genre_scores_gemma":[0.7045045,0.000005252434,0.2952152,0.00001556216,0.00009973611,0.00002888372,0.0000656364,0.000007144022,0.00005800268],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9682564,"threshold_uncertainty_score":0.167008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05094480243906481,"score_gpt":0.2996841361697309,"score_spread":0.2487393337306661,"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."}}