{"id":"W4402260639","doi":"10.1109/igarss53475.2024.10641308","title":"Identifying Burn Scars with ICEYE SAR Datasets","year":2024,"lang":"en","type":"article","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scars; Computer science; Synthetic aperture radar; Remote sensing; Artificial intelligence; Geology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005168281,0.0004153585,0.000277231,0.001068117,0.0002701854,0.0004179912,0.0003949427,0.0004161663,0.0009437316],"category_scores_gemma":[0.0008289381,0.0001794494,0.0002437145,0.0009767639,0.0001784007,0.0003717577,0.0003980994,0.0004513078,0.0003813461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003292073,"about_ca_system_score_gemma":0.0005329875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03214454,"about_ca_topic_score_gemma":0.06737081,"domain_scores_codex":[0.9997695,0.00002393563,0.00001427849,0.00005588848,0.00007807485,0.00005827794],"domain_scores_gemma":[0.9995697,0.0000597098,0.00005452928,0.000117916,0.0001622763,0.00003580397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001624847,0.001371097,0.3452083,0.0003995588,0.0004148225,0.001864059,0.0006934549,0.2373019,0.09820257,0.001701128,0.04734025,0.263878],"study_design_scores_gemma":[0.0001123392,0.0001965675,0.583225,0.00008671853,0.00007128716,0.0005136314,0.0007740055,0.3702159,0.02463808,0.00073308,0.0193691,0.00006420083],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9451699,0.0003687768,0.01977154,0.0001285513,0.00004233031,0.0001929695,0.02864473,0.001147199,0.004533926],"genre_scores_gemma":[0.8629676,0.0002319101,0.04576137,0.00006933403,0.00002424992,0.00009340874,0.08915544,0.0001195466,0.001577155],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03214454,"threshold_uncertainty_score":0.0639149,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009094861443625422,"score_gpt":0.2379426485356427,"score_spread":0.2288477870920173,"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."}}