Observations and modeling of turbulent fluxes during melt at the shrub-tundra transition zone 1: point scale variations
Bibliographic record
Abstract
Vegetation has a significant influence on snow accumulation and energy availability for snowmelt. This is particularly true in the vicinity of the arctic treeline, characterized by the alternation of shrub-tundra and open-tundra, with the former expected to spread more and more. This work considers the time variation in turbulent fluxes over two open-tundra and shrub-tundra sites, where measurements of sensible and latent heat fluxes over the canopy are available. An improved version of the GEOtop hydrological model with a dual-layer surface scheme has been used to interpret and reproduce the measurements. The model allows us to separate the contribution of the vegetation and the surface to the turbulent fluxes measured above the canopy and, despite some issues related to the parameterization of the turbulence in the canopy, is able to reasonably reproduce the turbulent fluxes measured above the vegetation and the snowmelt acceleration observed in the shrub-tundra. The maximum energy contribution to the surface during snowmelt is found to occur for values of the leaf and stem area index around 1.0. The model proves to be a valuable platform to be applied in a distributed model to predict the spatial variability of snowmelt and surface energy balance.
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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 | 0.000 | 0.000 |
| 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".