Can snow persistence explain the spatial-temporal variabilities in streamflow hydrograph flashiness across snow-dominated regions?
Bibliographic record
Abstract
Recent research showed that, snow persistence, defined here as the fraction of time that snow is present on the ground, can play an important role in explaining spatial variability of average annual streamflow in moderately snowmelt-dominated regions. Here, we extend this work and explore the following questions: 1) whether globally available snow persistence data is useful for estimating a suite of streamflow signatures explaining the shape, flashiness and components of streamflow hydrograph, and 2) whether snow persistence could be useful for reconstructing streamflow patterns in ungauged watersheds, both spatially and temporally. We explore these questions across a spectrum of climatic dryness, snowiness, and geological settings. The motivations for the study are the need to understand how loss of snow may affect the components of streamflow in different climatic and geological settings, as well as the need for simple methods to predict components of streamflow in snow-dominated ungauged basins.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".