{"id":"W1606999916","doi":"10.3390/rs70607615","title":"A Collection of SAR Methodologies for Monitoring Wetlands","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":213,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ducks Unlimited Canada; Government of Canada; Natural Resources Canada","funders":"","keywords":"Wetland; Remote sensing; Thresholding; Environmental science; Synthetic aperture radar; Vegetation (pathology); Change detection; Land cover; Land use; Computer science; Geography; Ecology; Artificial intelligence; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047279,0.00005764153,0.00009953586,0.00002927196,0.00005131692,0.000009655478,0.00004365381,0.00002782635,0.000003359308],"category_scores_gemma":[0.00009966204,0.00005136615,0.00003346297,0.0001295713,0.00003009595,0.00006434644,0.0000718564,0.00002931914,0.000005870731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001035628,"about_ca_system_score_gemma":0.00000602882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004532004,"about_ca_topic_score_gemma":0.00003690321,"domain_scores_codex":[0.9994748,0.00004232307,0.0001058033,0.0001204011,0.0001348333,0.000121881],"domain_scores_gemma":[0.9997327,0.00006511714,0.00005890089,0.0001003722,0.0000112604,0.00003167669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001369677,0.00002061521,0.006280411,0.000042874,0.00005171981,0.000004911044,0.001474146,0.02383548,0.06608115,0.00003308288,0.006470014,0.8955686],"study_design_scores_gemma":[0.001808729,0.0004913192,0.01104091,0.00009638482,0.0001304084,0.00001222109,0.003025953,0.6641345,0.2794327,0.01191991,0.02748754,0.0004193626],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5431753,0.0000275087,0.4475219,0.0001569432,0.0005149499,0.0002384134,3.494906e-7,0.00005055484,0.008314101],"genre_scores_gemma":[0.5320687,0.00001355575,0.4673584,0.00000787679,0.00004871506,1.568296e-8,7.536535e-7,0.000005828872,0.0004961292],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8951493,"threshold_uncertainty_score":0.2094651,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09453056206518459,"score_gpt":0.3400848198058319,"score_spread":0.2455542577406473,"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."}}