{"id":"W2149399328","doi":"10.1007/s13157-009-0016-z","title":"Hydrologic Dynamics of the Ground-Water-Dependent Sian Ka’an Wetlands, Mexico, Derived from InSAR and SAR Data","year":2010,"lang":"en","type":"article","venue":"Wetlands","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Space Agency; U.S. Geological Survey","keywords":"Wetland; Interferometric synthetic aperture radar; Hydrology (agriculture); Groundwater; Environmental science; Synthetic aperture radar; Surface water; Flooding (psychology); Remote sensing; Aquifer; Drainage basin; Geology; Water level; Geography; Ecology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006255908,0.000106198,0.00006736625,0.00036073,0.0001778792,0.0001887042,0.000104223,0.00008994535,0.0003668916],"category_scores_gemma":[0.0001561426,0.00007007288,0.00007515075,0.0003821043,0.0001129847,0.0001498124,0.0001122894,0.00009236105,0.00003658549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000556699,"about_ca_system_score_gemma":0.0003574631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0989927,"about_ca_topic_score_gemma":0.177665,"domain_scores_codex":[0.9999839,0.00000168668,0.000001112073,0.000005690706,0.000002585369,0.000004970243],"domain_scores_gemma":[0.9999326,0.0000122033,0.00002596512,0.000004317634,0.00001498485,0.00000979879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002165375,0.0001299213,0.9522778,0.00004214526,0.000109653,0.0003304491,0.0003897004,0.01574912,0.01156158,0.0005772613,0.001467172,0.0171486],"study_design_scores_gemma":[0.000005913456,0.000006786868,0.9913605,0.000001934722,0.00001720306,0.00001960116,0.00009690911,0.00755809,0.0003696316,0.00002178502,0.0005382574,0.000003369138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991585,0.00001358235,0.0000792178,0.0000145119,0.00000102894,0.00000183151,0.0003863246,0.000006620715,0.0003382389],"genre_scores_gemma":[0.998625,0.00003714985,0.0001766091,0.000003703165,0.000002531519,0.00000539034,0.0008720018,0.00000162056,0.0002760307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0989927,"threshold_uncertainty_score":0.196833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165213227311195,"score_gpt":0.2203507309518882,"score_spread":0.2086985986787762,"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."}}