Efectos hidrológicos de la conversión del bosque de niebla en el centro de Veracruz, México
Bibliographic record
Abstract
The provision and regulation of water flows in catchments is probably the most important ecosystem service of cloud forests; however, its hydrological behavior and impacts associated with forest conversion remain very poorly understood.The present study aimed at evaluating the hydrological effects of land use change for a cloud forest region on volcanic soils in Veracruz, Mexico.For this, micrometeorological, ecophysiological and hydrological measurements combined with stable isotope data were used.The findings showed higher annual water yields in pasture, as well as young and mature Pinus patula pine plantations due to lower evapotranspiration rates as compared to mature and secondary cloud forests.Total annual and seasonal flows were found very similar in both cloud forests, suggesting catchment hydrological functioning can be restored within 20 years of natural regeneration.Conversely, the pasture catchment showed higher annual streamflow (10 %), however 50 % on average lower baseflow at the end of the dry season, associated probably with more gentle slopes in combination with lower soil infiltration capacity.Further, it was shown that the conversion of cloud forest to pasture can promote major increases in overland flow in response to maximum rainfall events, despite the high permeability of the volcanic soils characterizing this environment.The ultimate effect of P. patula reforestation at catchment scale is still unknown, though higher rainfall infiltration rates, compared to pasture, suggest a soil hydrological recovery in the short to medium term.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".