Rapid soil water recovery after conversion of introduced peashrub and alfalfa to natural grassland on northern China’s Loess Plateau
Bibliographic record
Abstract
To evaluate the potential of soil water recovery after thinning, in situ soil water content in the 0–500 cm soil profile under thinned (50%–100%) and unthinned peashrub and alfalfa plots and a nearby natural grassland in the Liudaogou watershed in China’s Loess Plateau (CLP) was measured monthly during 2015–2017 growing season using a neutron probe. At the start of experiment, the profile soil water storage (SWS0–500 cm) under introduced peashrub and alfalfa was, respectively, 18.8% and 12.2% lower than that under natural grassland. This showed that there was higher water consumption by planted vegetation, compared with native grass. After thinning, SWS0–500 cm in thinned peashrub and alfalfa plots was significantly higher than that in unthinned plots due to decrease in both interception and transpiration. The increase in SWS0–500 cm in the 100% thinned peashrub plot (159.9–216.1 mm) was much higher than that in 50% thinned peashrub (39.1–169.8 mm) and 100% thinned alfalfa (20.3–118.1 mm) plots. This indicated that the extent of soil water recovery varied with thinning intensity and vegetation type. At the end of the third growing season, soil water restoration frontier in the thinned peashrub and alfalfa plots (>300 cm) was much greater than that in the unthinned plots (<180 cm). It also indicated that with thinning, soil water (<300 cm) can recover rapidly following two successive wet years. The results suggested that concerns about soil desiccation and the potential impact on long-term sustainability of restored ecosystems on CLP were resolvable.
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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 | 0.000 |
| 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".