{"id":"W4394741528","doi":"10.1088/1748-9326/ad3e18","title":"Drought intensification in Brazilian catchments: implications for water and land management","year":2024,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Saskatchewan; University of Calgary","funders":"Grantová Agentura České Republiky; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Environmental science; Water resource management; Hydrology (agriculture); Land use; Land management; Climatology; Geology; Ecology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006865571,0.00009963473,0.00008771339,0.000145672,0.0001773877,0.00005481361,0.0001597187,0.00004732788,0.000481944],"category_scores_gemma":[0.000006781906,0.00007977305,0.00003932273,0.0001659986,0.0003835243,0.0002184576,0.0002276252,0.0001774331,0.0006051814],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003240175,"about_ca_system_score_gemma":0.000001324646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009006126,"about_ca_topic_score_gemma":0.00008349019,"domain_scores_codex":[0.9986662,0.00008187636,0.0001608216,0.0004717434,0.000204954,0.0004144107],"domain_scores_gemma":[0.9996001,0.00008417269,0.000008388371,0.0002273376,8.687754e-7,0.00007915544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009156224,0.000304912,0.6355859,0.00008448645,0.0001568376,0.00007278729,0.00288905,0.000811271,0.292336,0.0006868884,0.01988856,0.04709173],"study_design_scores_gemma":[0.0007337834,0.0001003392,0.8273578,0.00003453597,0.00005176036,0.00001622033,0.000415375,0.004238032,0.006425498,0.007065863,0.1531857,0.0003750786],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727688,0.0001442151,0.0008213518,0.02457783,0.00003644233,0.0004942404,0.00001554917,0.00002078398,0.001120793],"genre_scores_gemma":[0.9971699,0.0001852486,0.0004061037,0.000681498,0.00002492248,0.0002030059,0.0001207977,0.00001450978,0.001194061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2859105,"threshold_uncertainty_score":0.7778584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0202253129665019,"score_gpt":0.3085251994851078,"score_spread":0.2882998865186059,"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."}}