{"id":"W2986184875","doi":"10.1002/hyp.13646","title":"Evaluating soil moisture estimation from ground‐penetrating radar hyperbola fitting with respect to a systematic time‐domain reflectometry data collection in a boreal podzolic agricultural field","year":2019,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"Research and Development Corporation of Newfoundland and Labrador","keywords":"Hyperbola; Ground-penetrating radar; Reflectometry; Water content; Radar; Remote sensing; Geology; Soil science; Mathematics; Time domain; Geotechnical engineering; Geometry; Engineering; Computer science","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":[0.0005048361,0.0002256339,0.0004297159,0.00008602457,0.0001151714,0.000124907,0.0003474396,0.000135881,0.00002225251],"category_scores_gemma":[0.001135455,0.0001531091,0.00002562295,0.001340524,0.00001525897,0.0002495781,0.00009261344,0.0003224853,0.00006117543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007691716,"about_ca_system_score_gemma":0.00003487194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002661147,"about_ca_topic_score_gemma":0.000124209,"domain_scores_codex":[0.9983452,0.0001474654,0.0004108772,0.000490781,0.000310411,0.0002952714],"domain_scores_gemma":[0.9980939,0.001269181,0.0001060521,0.0003816414,0.00007187651,0.00007732126],"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":[0.0004306084,0.0005318943,0.004726459,0.01925935,0.0002914753,0.00002268678,0.003393525,0.1328374,0.8298442,0.0003265922,0.0005040319,0.007831805],"study_design_scores_gemma":[0.00204153,0.002045115,0.1187101,0.006460034,0.0002226621,0.00006525935,0.001171252,0.8527882,0.006499408,0.008504418,0.00002878663,0.001463239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9918877,0.0001804246,0.00471537,0.0001873578,0.00003091821,0.0009328999,0.00001099538,0.000241122,0.001813229],"genre_scores_gemma":[0.9223016,0.000003470754,0.07716771,0.00008762274,0.0000849191,0.0001929057,0.0001136694,0.00001880793,0.00002927169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8233448,"threshold_uncertainty_score":0.6243606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.035045462362765,"score_gpt":0.3123883594035781,"score_spread":0.2773428970408131,"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."}}