{"id":"W4408392341","doi":"10.1016/j.jhydrol.2025.133068","title":"Bias correcting the precipitation dynamics of regional climate models via kernel-aware 2D convolutional-long short-term memory","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Environmental Security Technology Certification Program","keywords":"Kernel (algebra); Term (time); Precipitation; Climatology; Environmental science; Dynamics (music); Computer science; Econometrics; Meteorology; Mathematics; Geology; Geography; Psychology; Physics","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":[0.000241418,0.0007265097,0.0002698334,0.0002754558,0.0001860486,0.0003842317,0.000887604,0.0004335168,0.001046409],"category_scores_gemma":[0.0008867055,0.0002970103,0.0006352272,0.0003670212,0.0002227066,0.0007244194,0.000576181,0.0007494707,0.0003022847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006108761,"about_ca_system_score_gemma":0.0008422112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01628594,"about_ca_topic_score_gemma":0.02148306,"domain_scores_codex":[0.999899,0.00001200288,0.0000064305,0.00003967757,0.00002228326,0.00002052553],"domain_scores_gemma":[0.9998559,0.00003677871,0.00002465102,0.00002380103,0.00004915099,0.000009769656],"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":[0.00009052161,0.00005753936,0.004472761,0.00005940647,0.0001058518,0.0001137514,0.00005916793,0.8620312,0.01243557,0.002056528,0.002340776,0.116177],"study_design_scores_gemma":[0.000002570353,0.000006383503,0.0003203668,0.000001731902,0.000006450618,0.000006701967,0.000002325396,0.9978036,0.001265526,0.0003979726,0.000183756,0.000002525946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2229783,0.0007415906,0.7670547,0.0004772797,0.0001769036,0.00004099393,0.0007201403,0.004156653,0.003653464],"genre_scores_gemma":[0.9401228,0.0002206178,0.05617826,0.0001242741,0.00003087249,0.00004608365,0.0008199,0.0001143022,0.002342847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01628594,"threshold_uncertainty_score":0.03238225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04265376552992137,"score_gpt":0.2665803921913782,"score_spread":0.2239266266614568,"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."}}