Decadal hydroclimatic changes in the Pantanal, the world’s largest tropical wetland
Bibliographic record
Abstract
The Pantanal, the world's largest tropical wetland, experienced unusual drying in 2000-2021, but the causes are poorly understood. Combining remotely sensed data of wetland extent and land cover with observed water level discharge and meteorological data, we quantify the relative contributions of climate and land use to changes in Pantanal wetland extent. Climate variability drove 96% of the runoff changes over four major hydroclimate regimes, including two wet (1951-1964; 1976-2000) and two dry (1965-1975; 2001-2021) periods. Reduced precipitation, runoff, and wetland shrinkage observed in 2001-2021 resembled the previous dry period (1965-1975), indicating decadal climatic variability. However, the higher aridity index in the recent period exacerbated the duration of the drought, and the rainfall-runoff relationship shifted over time, with more runoff for a given rainfall amount in recent periods. Wetland area is highly sensitive to climate variability, contracting to 25% of the maximum during dry years. Future warming and reduced rainfall will likely continue the recent drying trend, further reducing runoff, wetland area, and the Pantanal biodiversity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".