{"id":"W2123778657","doi":"10.1109/jstars.2011.2116769","title":"Mapping Soil Moisture Using RADARSAT-2 Data and Local Autocorrelation Statistics","year":2011,"lang":"en","type":"article","venue":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Synthetic aperture radar; Environmental science; Remote sensing; Water content; Backscatter (email); Statistic; Moisture; Radar; Meteorology; Geology; Statistics; Geography; Mathematics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0002574926,0.0002445406,0.0001392818,0.0007771264,0.0001033379,0.0003361253,0.0002097073,0.0001204013,0.0003021752],"category_scores_gemma":[0.001013929,0.00009092977,0.0001234477,0.0007627553,0.0001438356,0.0003439493,0.0001550258,0.00007608368,0.0001123995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006575928,"about_ca_system_score_gemma":0.0006499193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1807141,"about_ca_topic_score_gemma":0.3243978,"domain_scores_codex":[0.9999089,0.00001875889,0.000003156825,0.00002193718,0.00003163315,0.00001573214],"domain_scores_gemma":[0.9997581,0.00007190897,0.00005420236,0.000035627,0.00006504407,0.00001511921],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002873579,0.00009456566,0.4052835,0.0001190345,0.0001359201,0.0002944545,0.0002373765,0.3580306,0.03209171,0.001017207,0.0009949532,0.2014133],"study_design_scores_gemma":[0.00002177477,0.00004892862,0.2852864,0.0000108068,0.0000334386,0.00005256894,0.0001234854,0.7091007,0.004210032,0.0003243632,0.0007664833,0.0000209263],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785954,0.00008758762,0.01907142,0.00002208303,0.000002379982,0.00001425103,0.0006261205,0.0002794594,0.001301275],"genre_scores_gemma":[0.9938245,0.00005578673,0.005178798,0.000004286862,0.000002159109,0.000003740969,0.0006753881,0.00001188934,0.0002434288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1807141,"threshold_uncertainty_score":0.3593244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05005717645828999,"score_gpt":0.2386566501975908,"score_spread":0.1885994737393008,"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."}}