{"id":"W2259958711","doi":"","title":"Climate change impacts on hydrometeorological variables using a bias correction method: The lake Karla watershed case","year":2015,"lang":"en","type":"article","venue":"Journal | MESA","topic":"Climate variability and models","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Downscaling; Hydrometeorology; Climate change; Precipitation; Climatology; Environmental science; Watershed; General Circulation Model; Mean radiant temperature; Climate model; Meteorology; Geography; Geology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004822091,0.0001839242,0.0002276419,0.0000590739,0.0004518562,0.0001580002,0.0001915825,0.0001332607,0.0009428729],"category_scores_gemma":[0.0004312099,0.0001024673,0.0001103023,0.0002549229,0.00009617172,0.0004134633,0.0001837226,0.0004621577,0.0001242024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003061456,"about_ca_system_score_gemma":0.0000156219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004450399,"about_ca_topic_score_gemma":0.0003194608,"domain_scores_codex":[0.9978251,0.0006986578,0.0003267095,0.0002555081,0.0004039658,0.0004900177],"domain_scores_gemma":[0.9988785,0.0003165344,0.0002029334,0.0002520081,0.00002093028,0.000329139],"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":[0.004590462,0.004168164,0.1228208,0.0001222275,0.0004674649,0.01870454,0.06052094,0.5631228,0.05232461,0.0009610875,0.03534169,0.1368551],"study_design_scores_gemma":[0.004089287,0.003027574,0.01656967,0.0002572215,0.0006043742,0.1301104,0.003342124,0.7768978,0.003937684,0.01641615,0.04324828,0.001499441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926098,0.0000211027,0.002346257,0.001055605,0.001032956,0.0002425291,0.00001314422,0.00003734963,0.002641246],"genre_scores_gemma":[0.993798,0.00006754947,0.004568395,0.001151175,0.0003115177,0.00001043606,0.000003242102,0.00001732915,0.00007239551],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2137749,"threshold_uncertainty_score":0.9999704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1902972339319885,"score_gpt":0.3373219436063034,"score_spread":0.1470247096743149,"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."}}