{"id":"W4410191434","doi":"10.1038/s41612-025-01033-9","title":"Developing an ensemble machine learning framework for enhanced climate projections using CMIP6 data in the Middle East","year":2025,"lang":"en","type":"article","venue":"npj Climate and Atmospheric Science","topic":"Climate variability and models","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Machine learning; Climatology; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001800723,0.0005651332,0.0004492255,0.0006040187,0.0003509394,0.0007049207,0.000773285,0.0005321405,0.0009267764],"category_scores_gemma":[0.002006978,0.0003242219,0.0006401681,0.0005920906,0.0001645745,0.0007952902,0.0007297533,0.0007350546,0.0001753803],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006009412,"about_ca_system_score_gemma":0.001015746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02171471,"about_ca_topic_score_gemma":0.0163968,"domain_scores_codex":[0.9997478,0.0001246451,0.00001222478,0.00003943713,0.00004490454,0.00003090069],"domain_scores_gemma":[0.9995669,0.0001803308,0.00004035765,0.00002946514,0.0001566268,0.00002642392],"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":[0.00002115639,0.00002854428,0.002349029,0.00001061318,0.00005486529,0.00002769582,0.00002469063,0.9699343,0.0003789505,0.001563478,0.0003704396,0.02523626],"study_design_scores_gemma":[9.631704e-7,0.000002914323,0.000172898,9.58168e-7,0.00000223747,0.000001083442,0.000003509308,0.9993703,0.00006110781,0.000322784,0.00006006852,0.000001245623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2893555,0.0003034587,0.7049013,0.0006271653,0.00006032238,0.00005767321,0.0005236575,0.0009718321,0.00319902],"genre_scores_gemma":[0.8817658,0.0001340941,0.1166391,0.0000468279,0.00004476948,0.00007711255,0.0004955372,0.00003925853,0.0007574676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02171471,"threshold_uncertainty_score":0.04317665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1103825993914569,"score_gpt":0.3380889991016879,"score_spread":0.2277063997102309,"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."}}