{"id":"W2103747227","doi":"10.1002/hyp.1021","title":"A multi‐sensor approach to wetland flood monitoring","year":2002,"lang":"en","type":"article","venue":"Hydrological Processes","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Saskatchewan","funders":"National Water Research Institute; University of Calgary","keywords":"Wetland; Environmental science; Delta; Remote sensing; Flood myth; Satellite imagery; Vegetation (pathology); River delta; Hydrology (agriculture); Geography; Geology; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001119658,0.0001746284,0.0001613381,0.00003172873,0.0001560685,0.00005749729,0.0003190289,0.00006994884,0.001031857],"category_scores_gemma":[0.0000911956,0.0001257175,0.00003519698,0.0003767568,0.00007765937,0.0001581497,0.0002922212,0.0001197524,0.00162607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003952981,"about_ca_system_score_gemma":0.000001565804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004362004,"about_ca_topic_score_gemma":0.0000103752,"domain_scores_codex":[0.9986426,0.00002791258,0.0001659547,0.0004770016,0.0002988516,0.0003877106],"domain_scores_gemma":[0.9995672,0.00003272605,0.00003915998,0.0001913334,0.000007754907,0.0001618032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001548198,0.01081694,0.74292,0.0005878777,0.0001814189,0.0001813452,0.005606645,0.1212311,0.03806974,0.000183179,0.0427415,0.03732536],"study_design_scores_gemma":[0.006663141,0.002880605,0.426369,0.0001282737,0.0003389522,0.00008312263,0.001771271,0.1807243,0.02185553,0.001447201,0.3533421,0.004396522],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9073394,0.0001924047,0.006616701,0.0006534255,0.0001209275,0.0005476688,0.000002050366,0.0002612617,0.08426614],"genre_scores_gemma":[0.9674152,0.0001149964,0.02962586,0.0003060765,0.00008936249,0.0001162443,0.000001721699,0.00001197524,0.002318606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3165511,"threshold_uncertainty_score":0.9998813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04291185612414479,"score_gpt":0.247689963550745,"score_spread":0.2047781074266002,"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."}}