Features of Oxygen and Hydrogen Isotopes in Waters from the Karst Mountains, Xiangxi River Basin
Bibliographic record
Abstract
Targeting the carbonate-dominated catchment of Xiangxi River, the first tributary of Three Gorges Reservoir, this paper explores the spatiotemporal distribution of stable isotopes in the stream water, and distinguishes the water sources and recharge processes in the river basin. Multiple water samples were collected from the catchments of the main tributaries of Xiangxi River, namely, Nanyang River and Gaolan River. The results show that the two tributary catchments had obvious spatial and seasonable variability in δ18O and δD. This is attributable to the seasonal changes in meteoric precipitation recharge, which depends on ambient temperature, precipitation, evaporation, and secondary evaporation of raindrops. Besides, there is a strong elevation effect on stable isotopes in stream water. In the samples from Gaolan River, the mean elevation effect on the δ18O and δD was -0.35‰, and -1.57‰ per 100m change in elevation in summer, and -0.12‰, and -1.19‰ per 100m change in elevation in winter, respectively. In the samples from Nanyang River, the mean elevation effect on the δ18O and δD was -0.14‰ and -0.89‰ per 100m change in elevation in summer, and -0.08‰ and -0.66‰ per 100m change in elevation in winter, respectively. The δD, δ18O, and deuterium excess (d-value) are not only affected by the elevation effect, but also by the seasonal effect in the study area. The seasonal variations and spatial changes in the tributary catchments reveal the difference between the two catchments in control factors like latitude effect, evaporation, and the mixing of different water sources.
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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".