Gap thinning improves soil water content, changes the vertical water distribution, and decreases the fluctuation
Bibliographic record
Abstract
Although it is clear that gap thinning significantly increases the soil water content (SWC) of the topsoil, less is known about whether and how this treatment affects deeper layers. From December 2008 to April 2012, we monitored the SWC at depths of 10, 20, 30, 45, 60, and 90 cm in gap creation treatments (small gap size of 30 m2, intermediate gap size of 80 m2, and unthinned plots) in a typical pine plantation in the eastern Tibetan Plateau. Among gap treatments, differences in SWC and its coefficient of variation (CV) at each depth and the soil water content proportion (SWCP) of the whole soil profile at specific depths were compared. Gap thinning improved SWC and decreased the CV at each depth. The SWCPs in thinned plots were lower at depths from 10 to 30 cm compared with unthinned plots but higher at depths of 45 and 60 cm. Also, in each season, the patterns were similar to the general results. In conclusion, gap thinning improves the SWC, changes the vertical soil water distribution, and decreases the SWC heterogeneity. The soil water conditions in intermediate gaps are more appropriate for local forest restoration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".