Influence of sub-cloud secondary evaporation and moisture sources on stable isotopes of precipitation in Shiyang River Basin，Northwest China
Bibliographic record
Abstract
Abstract The fractionation of stable isotopes in precipitation runs through all links of the hydrological cycle. Studying its composition will help to understand the hydrological cycle process and the interaction between land and atmosphere. Based on the data of measured precipitation isotopes and related meteorological elements from 11 sampling points from January 2018 to September 2019, the existence of sub-cloud secondary evaporation is verified. Used the water vapor flux and the improved Lagrangian model, the moisture source affecting precipitation is tracked. On the basis of them, the influence of sub-cloud secondary evaporation and moisture sources on stable isotopes of precipitation is analyzed. The results show that the sub-cloud secondary evaporation exists in the Shiyang River Basin, and it is stronger in spring and summer than that in autumn and winter, which makes the stable isotopes of precipitation higher in summer and lower in winter. Besides, the sub-cloud secondary evaporation is stronger in the midstream and downstream, which results in the heavy isotopes of precipitation are generally more enriched. In the vertical direction, the secondary evaporation between 850 hPa and 700 hPa is the strongest, which makes heavy isotopes enrich and d-excess decreases in this layer. The moisture source of precipitation in the Shiyang River Basin is dominated by westerly air masses, that mid-high latitude continental sources have a large contribution to precipitation but the supply of sea sources is very limited, which makes the d-excess of precipitation is higher and does not show regional consistency and seasonality well.
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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".