{"id":"W4417258390","doi":"10.1016/j.asr.2025.12.034","title":"Analyzing climatic anomalies and ecological impacts on wetlands environmental conditions using LSTM and remote sensing imagery","year":2025,"lang":"en","type":"article","venue":"Advances in Space Research","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Wetland; Context (archaeology); Normalized Difference Vegetation Index; Anomaly (physics); Vegetation (pathology); Satellite imagery; Anomaly detection; Satellite; Warning system","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":[],"consensus_categories":[],"category_scores_codex":[0.000785573,0.0001195304,0.0001608962,0.0002218213,0.0003403593,0.00008297557,0.00008496633,0.00004566907,0.00008940257],"category_scores_gemma":[0.00008351909,0.0001037047,0.00001930781,0.0003466767,0.0005717826,0.0003801236,0.0004732,0.0002575743,0.00001331859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002949722,"about_ca_system_score_gemma":0.000009085572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001552755,"about_ca_topic_score_gemma":0.0004759229,"domain_scores_codex":[0.998596,0.0001849142,0.0001566481,0.000361958,0.0002733553,0.0004271649],"domain_scores_gemma":[0.9993321,0.0003883715,0.00003573926,0.0001653546,0.000002929415,0.0000755159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001007966,0.0002008692,0.877226,0.0001564549,0.00003454927,0.0001829813,0.0003873766,0.004322788,0.02367205,0.001419707,0.0004052687,0.09189116],"study_design_scores_gemma":[0.001102213,0.0003229547,0.871008,0.0003756211,0.00003222316,0.00001483484,0.003312882,0.1032935,0.001096893,0.01513994,0.003929343,0.0003716781],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9868495,0.0009362184,0.0007304363,0.0006193166,0.00004183802,0.0002926239,0.000003488862,0.00001403549,0.01051258],"genre_scores_gemma":[0.9856703,0.005791323,0.008174955,0.00005383504,0.00001201921,0.000001926284,0.000004198353,0.000006832411,0.0002846441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09897067,"threshold_uncertainty_score":0.4228955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02345013378958827,"score_gpt":0.3756498906460982,"score_spread":0.3521997568565099,"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."}}