{"id":"W4409517354","doi":"10.1007/s00477-025-02987-1","title":"Development of an automatic time-series flood mapping framework using Sentinel-1 and 2 imagery","year":2025,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Flood myth; Series (stratigraphy); Remote sensing; Computer science; Computational intelligence; Time series; Artificial intelligence; Geology; Geography; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001440257,0.000280273,0.0003335968,0.0002415564,0.0008289149,0.0001195223,0.0002582245,0.0001004611,0.0004942953],"category_scores_gemma":[0.00004865246,0.0002521953,0.00003957945,0.0003150127,0.0008963607,0.0004445285,0.001386841,0.0004280634,0.00003078397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003660376,"about_ca_system_score_gemma":0.00006813798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001218828,"about_ca_topic_score_gemma":0.00003097453,"domain_scores_codex":[0.9971261,0.0002144038,0.0005066919,0.0006484506,0.0008898697,0.0006144896],"domain_scores_gemma":[0.9990193,0.000205312,0.0001502077,0.0003871386,0.000008325746,0.000229699],"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":[0.0002966237,0.005161,0.3547555,0.0008452412,0.001324971,0.00008719462,0.006759956,0.004772143,0.1748006,0.003360232,0.0006030783,0.4472335],"study_design_scores_gemma":[0.001577556,0.0004717631,0.8032662,0.0005089263,0.0001717494,0.00001431548,0.007867936,0.1685002,0.002303323,0.01352517,0.001023741,0.0007691163],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.937183,0.0001917278,0.06065762,0.00007238461,0.00006136536,0.0006800422,0.00001335436,0.00003323396,0.001107289],"genre_scores_gemma":[0.7718142,0.000292083,0.2271772,0.00001989016,0.00002260406,0.00007194558,0.00002087375,0.0000234266,0.0005577722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4485107,"threshold_uncertainty_score":0.999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01734986096801676,"score_gpt":0.3221246673184095,"score_spread":0.3047748063503927,"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."}}