Applying stable water isotopes to determine root water uptake patterns in an urban forest land
Bibliographic record
Abstract
The scarcity of water resources is an important issue in urbanization. Urban forest land water consumption accounts for a large part of urban water resources, the study of water uptake patterns in urban forest area is crucial for urban water saving and precision irrigation, but no identified research have investigated water uptake patterns in urban forest area until now. In this study, we measured the deuterium isotope ratio (δD) and the oxygen isotope ratio (δ18O) of precipitation, irrigation water, xylem water and soil water sources in a locust tree forest in Jinnan District of Tianjin City, China across 2019-2020. Water sources proportion in the root zone area of different growing seasons were obtained by IsoSource model, MixSIR model and SIAR model. Results show that there is a significant difference in soil moisture content between different stand age locust trees in time and depth variation. The trend of soil moisture of different stand ages of locust in time sequence intend to increase first and then decrease, the most significantly change of soil water content happened in shallow layer (0~40 cm). The change in vertical depth is about the same. The soil profile of 0-200 cm was discretized into three layers. The shallow layer (0~40 cm) soil water δD and δ18O fluctuated widely and decreased with the depth increased. This study revealed the dynamic replenishment of the root zone water in urban forest land, and provides insights into reforestation and water management in urban area.
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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".