{"id":"W1968351819","doi":"10.1002/hyp.1351","title":"An analysis of the ground‐penetrating radar direct ground wave method for soil water content measurement","year":2003,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ground-penetrating radar; Loam; Reflectometry; Geology; Remote sensing; Calibration; Water content; Sampling (signal processing); Time domain; Radar; Soil science; Environmental science; Soil water; Optics; Geotechnical engineering; Mathematics; Engineering; Physics; Statistics; Telecommunications","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":[0.001403264,0.0003565491,0.0001995376,0.0007076201,0.0001018256,0.0003568966,0.0003683151,0.0004022027,0.0007077172],"category_scores_gemma":[0.003916477,0.0002158646,0.0002958113,0.0006627135,0.0002045089,0.0003222561,0.0002229599,0.0002422712,0.0003632225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002652222,"about_ca_system_score_gemma":0.000211793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001224591,"about_ca_topic_score_gemma":0.00126324,"domain_scores_codex":[0.9985066,0.000290258,0.00003229795,0.0001802655,0.0009514798,0.00003904938],"domain_scores_gemma":[0.9967905,0.001398907,0.0003110035,0.0002590761,0.001194403,0.00004607466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008049702,0.000100817,0.02660043,0.0002972581,0.0001090073,0.0002093869,0.0001198673,0.006676607,0.7178414,0.0005175024,0.0005845207,0.2461382],"study_design_scores_gemma":[0.00008511501,0.001851938,0.169089,0.00004529128,0.0002765981,0.001332909,0.0001744411,0.3026895,0.5182065,0.000367523,0.005784976,0.00009612032],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6684927,0.0009891413,0.3278258,0.00009944508,0.00006756499,0.0001368411,0.000227223,0.0004214313,0.001739714],"genre_scores_gemma":[0.8904548,0.000398602,0.1072909,0.00005194025,0.00002190042,0.00008081544,0.0002739419,0.0000717272,0.001355434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001403264,"threshold_uncertainty_score":0.007421196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09062440371918178,"score_gpt":0.2884385136477916,"score_spread":0.1978141099286098,"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."}}