{"id":"W371451046","doi":"","title":"Soil moisture retrieval using L-band time-series SAR data from the SMAPVEX12 experiment","year":2014,"lang":"en","type":"article","venue":"EUSAR 2014; 10th European Conference on Synthetic Aperture Radar; Proceedings of","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Synthetic aperture radar; Remote sensing; Water content; Environmental science; Radar; Vegetation (pathology); Inversion (geology); Moisture; Soil science; Meteorology; Geology; Geography; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001015485,0.0004810384,0.0004814008,0.00003946586,0.0003654024,0.0001838943,0.001574975,0.0001462233,0.0006948979],"category_scores_gemma":[0.0006551246,0.0003108877,0.0001073548,0.0001897273,0.0007378539,0.0003345049,0.0006863268,0.0004673539,0.001116136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000832693,"about_ca_system_score_gemma":0.00003332656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003022969,"about_ca_topic_score_gemma":0.00001849062,"domain_scores_codex":[0.9970744,0.0001522998,0.0004933612,0.0009834206,0.0008264531,0.0004700943],"domain_scores_gemma":[0.9981154,0.0003017136,0.0004069547,0.0009208879,0.00007613353,0.0001788642],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004012882,0.0002017427,0.001507337,0.00003506466,0.0001184987,0.00001144072,0.003498133,0.00002625221,0.9233923,0.0005649875,0.05343198,0.01681099],"study_design_scores_gemma":[0.001669247,0.0007691341,0.01704538,0.001201773,0.0003941098,0.0001382769,0.002781902,0.008369696,0.2095891,0.0009514093,0.7551602,0.00192983],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4417011,0.000559304,0.0005650111,0.005048815,0.0006712981,0.0006916859,0.00008605648,0.0002040785,0.5504726],"genre_scores_gemma":[0.991787,0.0001047575,0.004697772,0.001061902,0.0004918926,1.636742e-7,0.00003863484,0.00009857066,0.001719315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7138032,"threshold_uncertainty_score":0.9999343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02893530953440176,"score_gpt":0.2349377518090051,"score_spread":0.2060024422746033,"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."}}