Analyzing water uptake of apple trees using isotopic techniques in the Shandong Peninsula, China
Bibliographic record
Abstract
Study region The hilly area of Shandong Peninsula is a pivotal apple-producing region in China. However, the precise water sources utilized by the apple trees for transpiration remain poorly understood in this region. Study focus Here we quantify the water sources used by apple trees in this area using stable isotopic tracing methods. Through on-field studies in a representative apple orchard and subsequent isotopic assessments, the primary water sources tapped by the apple trees were identified in three plots with contrasting soil characteristics and through 5 days of sub daily sampling. New hydrological insights for the region Our results show that apple trees have a marked preference for soil water centered at the 60 cm depth, with more deep water use at plots without weathered layers. Notably, the isotopic compositions of the xylem water leaned more towards signatures of soil water, rather than immediate irrigation water or groundwater. Given the irrigation water used to be the dominant water source recharging into soil, the weak contribution of irrigation water to plant would be attributed to the high soil evaporation rates during the growth phase, which strongly alter the isotopic composition of irrigation water in shallow soil layers. These insights boosted our comprehension of water sourcing mechanisms in the sloped orchard ecosystems in the Shandong Peninsula and lay the groundwork for deeper exploration into the irrigation ratio to rainwater utilized by apple trees in comparable regions.
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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".