{"id":"W791978432","doi":"10.5589/m12-043","title":"Using RADARSAT-2 polarimetric and ENVISAT-ASAR dual-polarization data for estimating soil moisture over agricultural fields.","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Université de Sherbrooke","funders":"Natural Resources Canada; Agriculture and Agri-Food Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Surface roughness; Remote sensing; Polarimetry; Water content; Surface finish; Geography; Environmental science; Soil science; Mathematics; Scattering; Physics; Geology; Optics; Geotechnical engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.0002963928,0.0002771547,0.0001343433,0.0009685659,0.000240658,0.0004356134,0.0002252799,0.0002223735,0.0007193591],"category_scores_gemma":[0.0005105771,0.0001681765,0.0001661961,0.0008911871,0.0001096446,0.0006160309,0.0002246092,0.0001974081,0.0003361287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007564357,"about_ca_system_score_gemma":0.0009570642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2336651,"about_ca_topic_score_gemma":0.4842576,"domain_scores_codex":[0.9999174,0.000009748849,0.000004319289,0.00001831331,0.00003693662,0.00001332777],"domain_scores_gemma":[0.999895,0.00001840856,0.00001609604,0.00001109079,0.00004602897,0.00001344204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002667222,0.0002351105,0.2464506,0.0003603667,0.00039392,0.0002764522,0.0002038829,0.06009906,0.1095081,0.0007941675,0.006048009,0.5753636],"study_design_scores_gemma":[0.00008859655,0.00009301057,0.5814872,0.00006089988,0.0002513828,0.0001339021,0.0006682668,0.3826408,0.02066403,0.001132139,0.01269758,0.00008214363],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9260544,0.002079754,0.05210776,0.0004270417,0.00007637219,0.0001562418,0.008409687,0.0009982036,0.00969057],"genre_scores_gemma":[0.9071026,0.0008838823,0.08218104,0.00009834125,0.0000215167,0.00003218677,0.006408731,0.00005404363,0.003217671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2336651,"threshold_uncertainty_score":0.46461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02323631833836185,"score_gpt":0.2414132235170605,"score_spread":0.2181769051786986,"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."}}