Scale Dependent Relationships Between Precipitation and Atmospheric Source Nitrate: Insights From the Yarlung Tsangpo River Basin
Bibliographic record
Abstract
Abstract Precipitation can increase the fluxes of both atmospheric‐source and biological‐source nitrate to rivers simultaneously, making it unclear whether precipitation increases the relative contribution of atmospheric‐source nitrate in rivers. To gain a deeper insight into this issue, this study leveraged the significant precipitation gradient of the Yarlung Tsangpo River Basin, which spans the entire southern Tibetan Plateau, to explore the impact of precipitation on the relative contribution of atmospheric‐source nitrate (fatm) in rivers using the Δ17O–NO3− approach. The results showed that fatm can be as high as 20% particularly in areas with substantial precipitation, glacier and permanent snow coverage and lower nitrogen concentrations. fatm exhibited a strong positive correlation with precipitation in the Yarlung Tsangpo River Basin, although this relationship varied across individual rivers. Predictive models were developed to estimate the fatm in unsampled rivers, which could be valuable for watershed management and nitrogen budget calculations.
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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".