{"id":"W4391322571","doi":"10.23818/limn.43.22","title":"Disclosing the effects of climate, land use, and water demand as drivers of hydrological trends in a Mediterranean river basin","year":2024,"lang":"en","type":"article","venue":"Limnetica","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 European Research Council; Generalitat de Catalunya; Agència Catalana de l'Aigua; Canadian Institute for Advanced Research","keywords":"Mediterranean climate; Environmental science; Water resource management; Mediterranean Basin; Drainage basin; Climate change; Land use; Structural basin; Hydrology (agriculture); Geography; Geology; Ecology; Oceanography; Archaeology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001704971,0.00008441936,0.0001395761,0.00005055708,0.00004941752,0.00001114377,0.00007953377,0.00004693701,0.0001819299],"category_scores_gemma":[0.00001762582,0.00004245888,0.00002967039,0.00009521574,0.0006714381,0.00008624313,0.000251969,0.0000799141,0.00001945266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008023019,"about_ca_system_score_gemma":4.405166e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000119176,"about_ca_topic_score_gemma":0.00006168402,"domain_scores_codex":[0.9993267,0.0000805603,0.0001261596,0.0001804999,0.00009934016,0.0001867339],"domain_scores_gemma":[0.9996762,0.0001910632,0.00001493948,0.00009436748,0.000001062246,0.00002231733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001249105,0.0001125033,0.9674781,0.0002135895,0.0001048085,0.0001572109,0.01315064,0.0003592264,0.008669057,0.0003462723,0.0006892676,0.00859439],"study_design_scores_gemma":[0.0005133341,0.0004233665,0.9865475,0.00009096088,0.0001162308,0.000006250788,0.00005310321,0.001567594,0.00607822,0.001691957,0.002785548,0.0001259531],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952539,0.00008970869,0.000007672524,0.001073945,0.0000746498,0.00009138993,0.000001904052,0.00001075424,0.003396128],"genre_scores_gemma":[0.9994404,0.0002472945,0.00004179552,0.0001171823,0.000008692509,0.000006396052,0.00000265879,0.00000427929,0.0001313557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01906936,"threshold_uncertainty_score":0.2473942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008167391876929801,"score_gpt":0.2198154337761563,"score_spread":0.2116480418992265,"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."}}