{"id":"W4283813344","doi":"10.3390/rs14133210","title":"Soil Moisture Retrieval Using SAR Backscattering Ratio Method during the Crop Growing Season","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Western University","funders":"Fundamental Research Funds for the Central Universities; Canadian Space Agency; Natural Sciences and Engineering Research Council of Canada; University of Electronic Science and Technology of China; National Natural Science Foundation of China","keywords":"Remote sensing; Water content; Environmental science; Vegetation (pathology); Normalized Difference Vegetation Index; Growing season; Synthetic aperture radar; Soil science; Leaf area index; Agronomy; Geology","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":[0.0001798736,0.0002838038,0.0002641202,0.0005348111,0.00007495358,0.0002248256,0.0002024411,0.0002286778,0.0003159139],"category_scores_gemma":[0.0002649202,0.0001388493,0.0002417183,0.0004370346,0.0000853649,0.0005938892,0.0001364244,0.0001471589,0.0002025873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001153127,"about_ca_system_score_gemma":0.0001387516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001647148,"about_ca_topic_score_gemma":0.002200775,"domain_scores_codex":[0.9999013,0.00001542363,0.000005405625,0.00003467166,0.00003152197,0.00001171441],"domain_scores_gemma":[0.9999378,0.00001389494,0.00001495348,0.000007817401,0.0000216418,0.000003961938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0003675136,0.0001592256,0.05142273,0.0002397067,0.0001461855,0.000350803,0.0001963704,0.1173734,0.4697083,0.000846528,0.00160816,0.3575811],"study_design_scores_gemma":[0.00002863573,0.0000975115,0.0519685,0.000008713306,0.00004622642,0.000194308,0.00005637314,0.8967998,0.04965891,0.0002291275,0.0008794628,0.00003247677],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8181167,0.0003850306,0.1784681,0.00006227714,0.00004167304,0.00002857037,0.000328824,0.000885675,0.00168308],"genre_scores_gemma":[0.9602733,0.0002157065,0.03852032,0.00002428106,0.00001460531,0.00001386501,0.0003605504,0.00002953,0.0005478058],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001647148,"threshold_uncertainty_score":0.003275156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272276671689733,"score_gpt":0.2477627360890744,"score_spread":0.235039969372177,"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."}}