{"id":"W4404093532","doi":"10.1016/j.agwat.2024.109159","title":"Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index","year":2024,"lang":"en","type":"article","venue":"Agricultural Water Management","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Southwest Jiaotong University; National Natural Science Foundation of China","keywords":"Environmental science; Water content; Remote sensing; Vegetation (pathology); Vegetation Index; Enhanced vegetation index; Index (typography); Soil science; Normalized Difference Vegetation Index; Hydrology (agriculture); Leaf area index; Geology; Agronomy; Computer science; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001090773,0.0003010509,0.0001717727,0.0004195243,0.00009429018,0.0002990219,0.0001873096,0.0001361527,0.0001754519],"category_scores_gemma":[0.000165869,0.0001183906,0.0001951333,0.0003502195,0.00007879504,0.0003883415,0.000194017,0.0001355982,0.00008449823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001808401,"about_ca_system_score_gemma":0.0001971491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002912464,"about_ca_topic_score_gemma":0.004769782,"domain_scores_codex":[0.9999421,0.000006944606,0.000002409484,0.00001895352,0.00002207813,0.000007582093],"domain_scores_gemma":[0.9999514,0.000007899812,0.00001200681,0.000006479211,0.00001725571,0.000004950511],"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":[0.0002637037,0.0002023731,0.05804152,0.0001235031,0.0001637364,0.000223093,0.0001082487,0.07186294,0.5942869,0.0008152354,0.0009778626,0.272931],"study_design_scores_gemma":[0.00003727149,0.00009869169,0.04946396,0.000006298784,0.00005328792,0.0001472422,0.00007681046,0.8798054,0.06894867,0.000277333,0.001054419,0.00003072461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8934557,0.0002070108,0.1044945,0.00003990601,0.00001616274,0.00001833793,0.0002140045,0.0003260249,0.001228366],"genre_scores_gemma":[0.9561308,0.0001572717,0.04290122,0.00001437364,0.000009556834,0.00000953233,0.0003552145,0.00001568845,0.0004062896],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002912464,"threshold_uncertainty_score":0.005791008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007715295863304729,"score_gpt":0.2075422700654521,"score_spread":0.1998269742021474,"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."}}