Determining Water Use Patterns for the Betula platyphylla in the Southern Greater Khingan Mountains and Its Adaptation to Drought
Bibliographic record
Abstract
Due to global climate change, seasonal droughts have intensified and become more frequent in certain semi-arid regions, and plants often adjust their water uptake depths to adapt to shifting environmental conditions. The southern Greater Khingan Mountains have a large natural secondary forest area and act as an important ecological barrier. This study focused on examining the water use patterns of the primary plant species, Betula platyphylla, within the natural secondary forest of the southern Greater Khingan Mountains. The investigation utilized oxygen stable isotope techniques and covered a timeframe spanning from July 2019 to September 2020. The findings indicate that the adaptable water utilization strategies and rapid response to precipitation could facilitate plants fully utilizing water from all depths, thereby enabling them to better adapt to arid environments. When the rainfall was low (390.4 mm in the growing season of 2019), the developed shallow roots quickly absorbed shallow soil water (0–20 cm, with a utilization ratio of 40.4% for the entire root depth), but when the rainfall increased (501.5 mm in the growing season of 2020), Betula platyphylla gradually transitioned to extracting soil water from deeper soil layers (40–60 cm) and deepened its root system (with a utilization ratio of 39.4%), indicating its adaptability to semi-arid environments. Therefore, the flexible water use strategy of Betula platyphylla in the same habitat may give it a competitive advantage during low rainfall periods. The findings are important for the protection of natural forests and water management in the southern Greater Khingan Mountains.
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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 | 0.000 |
| 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".