{"id":"W2133690993","doi":"10.1109/igarss.2000.861668","title":"Application of neural networks for wetland classification in RADARSAT SAR imagery","year":2002,"lang":"en","type":"article","venue":"","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Canadian Space Agency","keywords":"Artificial neural network; Backpropagation; Computer science; Synthetic aperture radar; Artificial intelligence; Vegetation (pathology); Remote sensing; Machine learning; Data mining; Geography","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":[],"consensus_categories":[],"category_scores_codex":[0.0000863587,0.00009540634,0.0001335391,0.00007667079,0.00002120517,0.000008859571,0.0001038874,0.00008744802,0.00003143772],"category_scores_gemma":[0.000008625007,0.00008919365,0.00004262661,0.0001792833,0.00002670036,0.00006180294,0.000007493446,0.00006844466,0.000003018169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003411891,"about_ca_system_score_gemma":0.000001433195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002021721,"about_ca_topic_score_gemma":0.000009401125,"domain_scores_codex":[0.9994162,0.000007137064,0.0002500311,0.0001378803,0.00005766616,0.0001311013],"domain_scores_gemma":[0.9995567,0.00009986218,0.00003810985,0.0002553331,0.00002892933,0.0000210919],"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":[0.000004125173,0.00004924478,0.001025588,0.00003458442,0.000007946945,9.857752e-8,0.00003733475,0.0004519397,0.003480125,0.005008366,0.004115296,0.9857854],"study_design_scores_gemma":[0.0001236244,0.000009420781,0.00123015,0.000005151079,0.00000605923,0.000001812908,0.000014094,0.8612953,0.004555196,0.0003146356,0.1323563,0.00008826065],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003147886,0.0003100477,0.9914826,0.000324195,0.00003068719,0.0005160701,0.00000494311,0.0002314041,0.003952199],"genre_scores_gemma":[0.8288236,0.0001149786,0.1708319,0.0000343235,0.00004707154,0.00005371386,0.00001974722,0.00002095945,0.00005370161],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9856971,"threshold_uncertainty_score":0.3637211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01412676508142467,"score_gpt":0.222729376051946,"score_spread":0.2086026109705213,"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."}}