{"id":"W4408935624","doi":"10.1007/s12145-025-01845-6","title":"S-Transformer: a new deep learning model enhanced by sequential transformer encoders for drought forecasting","year":2025,"lang":"en","type":"article","venue":"Earth Science Informatics","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Transformer; Encoder; Computer science; Artificial intelligence; Engineering; Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005394241,0.0006817956,0.0005846585,0.0005407015,0.0001979812,0.0005777621,0.001155875,0.0005509116,0.001836349],"category_scores_gemma":[0.0009010346,0.0003133989,0.0005644595,0.0005507628,0.0002539246,0.0009617868,0.0006403954,0.000966246,0.0003544776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005991714,"about_ca_system_score_gemma":0.0009990942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01256285,"about_ca_topic_score_gemma":0.01334411,"domain_scores_codex":[0.9998826,0.00002222108,0.000009245526,0.00003250158,0.00003076301,0.00002262418],"domain_scores_gemma":[0.9997889,0.0000737333,0.00001916422,0.00001498423,0.00008687897,0.00001630878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001526041,0.0001111739,0.001458575,0.00005408539,0.00007357752,0.00006756696,0.0000201874,0.8756601,0.003355448,0.002117438,0.002100857,0.1148284],"study_design_scores_gemma":[0.000002458154,0.00001099507,0.00003784037,0.000001407197,0.000003493912,0.000003675649,7.967212e-7,0.9993297,0.000276479,0.0002341314,0.00009766894,0.000001303538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1109423,0.00136522,0.8793482,0.0004930283,0.0002783232,0.00008039149,0.0004844913,0.002758144,0.004249906],"genre_scores_gemma":[0.9224694,0.0005369436,0.07182907,0.0001508662,0.00004980469,0.00007648756,0.0006104821,0.00007543233,0.00420165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01256285,"threshold_uncertainty_score":0.02497941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01120912110236538,"score_gpt":0.2454186175111179,"score_spread":0.2342094964087525,"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."}}