Responses of root water uptake to soil water dynamics for three revegetation species on the Loess Plateau of China
Bibliographic record
Abstract
Abstract In water‐limited ecosystems, soil water regulates root water uptake (RWU) strategies. However, RWU responses to soil water changes under different species are not well‐understood. We assessed RWU responses of three revegetation species [shrub ( Hippophae rhamnoides Linn.), coniferous forest [ Platycladus orientalis (L.) Franco], and broad‐leaved forest ( Robinia pseudoacacia L.) during the dry (May to June) and rainy (July to August) seasons in 2020 on the Loess Plateau using stable isotope methods. We sampled soil and xylem for each species at approximately weekly intervals and used the MixSIAR model to quantify RWU contribution with stable water isotopes. The results indicated that soil water in the shallow (0–40 cm) and middle (40–200 cm) soil layers fluctuated more strongly than the deep soil layer (200–300 cm) due to precipitation and evapotranspiration. Before precipitation in the dry season, most of the RWU for H. rhamnoides and R. pseudoacacia (97% and 98%) came from the middle layer under limited soil water. After precipitation in the dry season, the three species had similar RWU responses to soil water changes. After precipitation in the rainy season, the RWU change of H. rhamnoides and R. pseudoacacia with deep soil drying was more sensitive to soil water change than P. orientalis with sufficient deep water on August 3 and 11, while, the RWU of H. rhamnoides was more sensitive to soil water change than R. pseudoacacia on August 11 and 19. Thus, by switching its water‐use strategy, H. rhamnoides adapted better to the soil water environment than P. orientalis and R. pseudoacacia . This finding will help in selecting the optimal revegetation species for water use in a changing climate environment.
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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 | 0.000 | 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".