Longer‐term effects of pine and eucalypt plantations on streamflow
Bibliographic record
Abstract
The longer‐term effects of afforestation with Pinus radiata and Eucalyptus grandis on streamflows were analyzed using data from two paired‐catchment experiments in South Africa. The experiments are rare in that they have been maintained over longer periods than the typical rotation period for industrial timber plantations in the tropics or subtropics. In both experiments the planting treatments led to large reductions in streamflow, which increased with the age of the trees and were positively related to water availability. The pine plantation caused peak reductions in yield over a 5‐year period of 44 mm a−1 or 7.7% a−1 for each 10% of catchment planted when the trees were between 10 and 20 years old. The eucalypt plantation caused peak reductions over a 3‐year period of 48 mm a−1 and 10% a−1 for each 10% of catchment planted. However, as the plantations matured (over 30 years of age in the case of pines and over 15 years of age in the case of eucalypts) the flow reduction trend was reversed, and streamflow effects appear to be tending toward preafforestation levels. The longer‐term effects of planted forests need not be as harmful on the water yield of catchments as has been predicted from shorter‐term studies. The implication of these results is that if trees are grown on very long rotations, they may be used for restoring degraded catchments or as a means of storing carbon without completely denuding available water resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".