Contribution of Recycled Moisture to Precipitation: A Modified D‐Excess‐Based Model
Bibliographic record
Abstract
Abstract Defining moisture sources and evolution of precipitation is of significance for further exploration of complex hydro‐climatic interactions, especially under global warming with alternations of evapotranspiration capacity and precipitation moisture source structure. As a sensitive indicator, d‐excess has been widely used to quantify the proportion of recycled vapor to precipitation (fre). However, existing d‐excess‐based models ignore to take transpiration vapor into account and the calculated fre tends to be lower than the true value, thus underestimating the importance of recycled vapor in precipitation. Herein, the existing model was modified with transpiration vapor considered and applied in Guyuan, China located in a monsoon marginal zone with complex precipitation moisture sources. After modification, the estimated annual average fre was increased from 7.5% to 14.8%, well calibrating the existing model. This study highlights the contribution of transpiration vapor to precipitation and provides more information on the formation and evolution of precipitation to better serve future hydro‐climatic research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".