Time and space catch up with restoration programs that ignore ecosystem service trade-offs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In response to extreme societal consequences of ecosystem degradation and climate change, attention to ecological restoration is increasing globally. In China, investments in restoration exceeded USD 378.5 billion over the past decade. However, restoration programs are experiments that can cause marked unintended consequences, with trade-offs across space and time that have undergone little empirical examination. We quantified the long-term effects of large-scale afforestation for soil erosion and sandstorm prevention in semiarid China. We found that soil erosion was notably reduced by afforestation but surface runoff declined significantly, after a time lag of 18 years, limiting overall benefit. While forest area also increased, forest quality declined, interacting with reduced surface water runoff. Crucially, increased forest water consumption accelerated downstream groundwater depletion, thus intensifying conflicts over water use. The time lags and spatial trade-offs revealed by this case study provide critical lessons for large-scale restoration programs globally.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| 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 it