Water budget fluxes in catchments under grassland and <i>Eucalyptus</i> plantations of different ages
Bibliographic record
Abstract
Planted forests are increasing in area worldwide in recent decades and play an important role in carbon sequestration programs. However, the effects of plantations on water resources are largely unknown. Here, we investigate the effects of Eucalyptus sp. plantations on water budget fluxes in the southern Brazilian grasslands biome. We evaluated green (canopy interception and evapotranspiration) and blue (discharge) water flows in three watersheds: two watersheds predominantly covered with eucalyptus, either in the first years after planting or at the end of the rotation, and one watershed with livestock-grazing grassland. We used field measurements of rainfall, streamflow, and throughfall and estimated canopy interception and evapotranspiration by water balance. Water flows in the monitored watersheds with eucalyptus plantations were influenced by forest developmental stage. Annual canopy interception and transpiration were always higher in the watersheds with eucalyptus than in the one with grassland, except for the transpiration in the first year after plantation in the watershed with young eucalyptus. An increase in evapotranspiration (green water flow) and the consequent decrease in streamflow (blue water flow) should be considered in local water resource management. Studies of catchment hydrology and forest management for improved water use efficiency and streamflow regulation are needed, particularly in understudied regions.
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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 | 0.000 |
| 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) | 0.000 | 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".