{"id":"W4240428843","doi":"10.1109/igarss.2005.1526103","title":"Flood and soil wetness monitoring over the Mackenzie River Basin using AMSR-E 37 GHz brightness temperature","year":2005,"lang":"en","type":"article","venue":"","topic":"Soil Moisture and Remote Sensing","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec","funders":"","keywords":"Environmental science; Brightness temperature; Hydrology (agriculture); Moderate-resolution imaging spectroradiometer; Rating curve; Remote sensing; Vegetation (pathology); Structural basin; Enhanced vegetation index; Flood myth; Drainage basin; Normalized Difference Vegetation Index; Brightness; Geology; Vegetation Index; Satellite; Geomorphology; Climate change; Geography; Sediment; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001582691,0.000204524,0.000155783,0.00001891353,0.0004426986,0.0001221787,0.0001662884,0.000127982,0.0001634489],"category_scores_gemma":[0.000009968774,0.0001221688,0.00005609693,0.0002031196,0.0003043353,0.0003158937,0.0001927079,0.0002351739,0.00005796231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001133395,"about_ca_system_score_gemma":0.000009272916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002696889,"about_ca_topic_score_gemma":0.001607141,"domain_scores_codex":[0.9987793,0.00005589124,0.0001623559,0.0003535858,0.000316058,0.0003328236],"domain_scores_gemma":[0.9995099,0.00004850812,0.00004744504,0.000288701,0.000008537725,0.00009688474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006088491,0.0002223462,0.5117698,0.000033537,0.0001235925,0.0001061549,0.006020745,0.01494518,0.3029959,0.0001244248,0.004534309,0.1590632],"study_design_scores_gemma":[0.0005382429,0.00001333097,0.8928996,0.00005095869,0.00005593433,0.000127445,0.0003643363,0.003786632,0.09260426,0.0001249644,0.009049678,0.0003846179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874175,0.0002009651,0.00003870754,0.0005894043,0.0003370671,0.0001221837,0.000001075321,0.00005374388,0.01123938],"genre_scores_gemma":[0.9954681,0.00004193879,0.002141158,0.0005040348,0.0005544912,4.196631e-7,0.000001320994,0.00002319565,0.001265349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3811298,"threshold_uncertainty_score":0.49819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009234158959415806,"score_gpt":0.2250682167972348,"score_spread":0.2158340578378189,"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."}}