{"id":"W4415976534","doi":"10.1016/j.rse.2025.115110","title":"Automated drone-borne GPR mapping of root-zone soil moisture for precision irrigation","year":2025,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Geophysical Methods and Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Fonds De La Recherche Scientifique - FNRS; Gouvernement Wallon","keywords":"Water content; Ground-penetrating radar; Reflectometry; Precision agriculture; Irrigation; Moisture; Radar; Soil water; Precipitation","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001351134,0.0001068664,0.0002125241,0.00005897162,0.00003840966,0.000004927639,0.00005650369,0.00007347207,0.000002382095],"category_scores_gemma":[0.00002522629,0.0001093298,0.00007433594,0.0001187267,0.00003706335,0.00002253939,0.00002748841,0.00006579205,0.000003138834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006305936,"about_ca_system_score_gemma":0.00000614197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004260749,"about_ca_topic_score_gemma":0.000003526327,"domain_scores_codex":[0.9993018,0.00002021404,0.0002926006,0.0001512714,0.0001108596,0.0001232468],"domain_scores_gemma":[0.9995045,0.0001151626,0.00007388437,0.000263562,0.00001630386,0.00002658728],"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":[0.000005546547,0.00002225754,0.00000357238,0.0001456046,0.00003608533,1.38173e-7,0.00006781468,0.05231138,0.7165958,0.0002530757,0.0001157637,0.2304429],"study_design_scores_gemma":[0.0003303265,0.00002822466,0.02624471,0.0002664645,0.00004738608,5.33908e-7,0.00003710118,0.7547378,0.2098409,0.005435996,0.002909057,0.0001214833],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3885745,0.00009556064,0.6101152,0.0001592802,0.00006918462,0.0002721078,0.000007835015,0.00009936999,0.0006068919],"genre_scores_gemma":[0.7833825,0.00001927657,0.2164364,0.000006352979,0.00002163853,5.78684e-7,0.00001415936,0.00001359576,0.0001055526],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7024264,"threshold_uncertainty_score":0.4458341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012443146122216,"score_gpt":0.2428372702082696,"score_spread":0.2327128387470474,"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."}}