Evapotranspiration partitioning based on field‐stable oxygen isotope observations for an urban locust forest land
Bibliographic record
Abstract
Abstract The stable water isotope method is widely applied to distinguish the evapotranspiration (ET) components across various vegetation‐covered surfaces. ET partitioning in the urban woodland area is useful for guiding precision irrigation, thereby promoting in urban water conservation. For the first time, this study partitions ET in urban locust forest areas based on stable water isotope observations for the period 2019–2020. The isotope composition of ET (δET) and soil evaporation (δE) were determined using the Keeling‐plot method and Craig–Gordon model, respectively. The steady‐state (SS) and the non‐steady‐state (NSS) assumptions were compared for estimating the isotope composition of vegetation transpiration (δT). The NSS outperformed SS in daily bulk leaf water isotopic component (δL,b) simulation and recommended to be used in δT determination for the urban forest land. Both methods resulted in similar estimates of δL,b and δT during the daytime; however, substantial difference was observed during the nighttime. The fraction of vegetation transpiration to evapotranspiration (FT) varies between 0.21 and 0.95 with an average value of 0.78 for the SS and varies between 0.22 and 0.97 with an average of 0.82 for the NSS. The FT in the urban forest land is higher than the natural forest land due to the urban heat island effect and higher planting densities. The seasonal FT variation is primarily controlled by the leaf area index (LAI) and soil moisture. The predictive uncertainty of δET and δT are much higher than δE, wherein the uncertainties of δT decreases as FT increases while the uncertainty of δET increases as FT increases. The uncertainty analysis highlights the importance of increasing the sampling frequency under low FT condition. This study revealed the seasonal change patterns of FT and its major governing factors in urban woodland areas, thus providing more insights on effective water management in the urban ecosystems.
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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) | 0.001 | 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".