{"id":"W1662286684","doi":"10.1029/2003wr002312","title":"Mapping near‐surface soil moisture with RADARSAT‐1 synthetic aperture radar data","year":2004,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Université de Montréal; École de Technologie Supérieure","funders":"","keywords":"Synthetic aperture radar; Water content; Remote sensing; Environmental science; Watershed; Vegetation (pathology); Backscatter (email); Soil science; Scale (ratio); Moisture; Surface roughness; Radar; Hydrology (agriculture); Geology; Meteorology; Geography; Geotechnical engineering; Cartography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002676305,0.0002866564,0.0001774991,0.0004999564,0.0001342435,0.0002777446,0.000253401,0.0001506088,0.0004484573],"category_scores_gemma":[0.0005530588,0.0001061396,0.0001092035,0.0004041945,0.0001681859,0.0002169357,0.0001522209,0.0001296174,0.0001512737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006550258,"about_ca_system_score_gemma":0.0006163868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1339798,"about_ca_topic_score_gemma":0.3154418,"domain_scores_codex":[0.9998958,0.00002080779,0.000002876596,0.00002225518,0.00004180785,0.0000164378],"domain_scores_gemma":[0.9998347,0.00003167469,0.00002179581,0.00002293034,0.00007473574,0.00001416187],"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":[0.0003122553,0.0002617207,0.196512,0.0001732287,0.0001486143,0.0003996475,0.0004112032,0.1225874,0.3742597,0.0005639116,0.001182055,0.3031884],"study_design_scores_gemma":[0.00007651935,0.0003229894,0.5360606,0.00001478815,0.00005754134,0.0001795325,0.0002631528,0.4143083,0.04610516,0.0003066366,0.002255837,0.00004887855],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970626,0.0000962214,0.02732118,0.00003064842,0.000003041059,0.0000874423,0.0006661214,0.0002828584,0.0008865916],"genre_scores_gemma":[0.9620222,0.00008381484,0.03644881,0.000011348,0.000003051675,0.00003908316,0.0008220539,0.00001437501,0.0005551794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1339798,"threshold_uncertainty_score":0.2663999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03832734656400738,"score_gpt":0.2747811213977956,"score_spread":0.2364537748337882,"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."}}