{"id":"W4402264254","doi":"10.1109/igarss53475.2024.10641504","title":"Patch-Based Cascade Forest Wetland Classification Based on Multi-Temporal SAR Images in Yellow River Delta","year":2024,"lang":"en","type":"article","venue":"","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Natural Resources","keywords":"Cascade; Delta; Wetland; River delta; Remote sensing; Synthetic aperture radar; Computer science; Environmental science; Geology; Ecology; Engineering","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":[0.0004946039,0.0004359519,0.0003872706,0.001091822,0.0002603008,0.0003463911,0.0003413933,0.0002670048,0.0004486506],"category_scores_gemma":[0.0004008745,0.0001525762,0.0004765838,0.0005540696,0.0001594054,0.0005570363,0.0002808877,0.0002235819,0.0001485396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001819864,"about_ca_system_score_gemma":0.0003461954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01039744,"about_ca_topic_score_gemma":0.0156586,"domain_scores_codex":[0.9998115,0.00002395568,0.000008452655,0.00006578406,0.00004815362,0.00004224659],"domain_scores_gemma":[0.9998108,0.00003629081,0.00002323597,0.00002633375,0.00007881647,0.00002464253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008418582,0.0003493101,0.09736363,0.0001900019,0.0001661515,0.0006995479,0.0003363876,0.130437,0.128563,0.0006931174,0.003550687,0.6368094],"study_design_scores_gemma":[0.00001893839,0.00009136637,0.07370588,0.00001404167,0.0000654353,0.0001676016,0.0001796553,0.913129,0.01163406,0.0002932137,0.0006816174,0.00001909684],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9187571,0.0002897189,0.07891733,0.00007525863,0.0000372863,0.00004618395,0.0002169106,0.0004002495,0.001260122],"genre_scores_gemma":[0.9732577,0.000120402,0.02562657,0.00001466291,0.00001336559,0.00001125884,0.0003118213,0.00001442323,0.0006299219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01039744,"threshold_uncertainty_score":0.02067381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.021023622689759,"score_gpt":0.2599615172418068,"score_spread":0.2389378945520478,"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."}}