Shrubs are widespread in Snowpacks and Affect Ground Temperature. Models Must Include this Process
Bibliographic record
Abstract
Shrubs covered by snow enhance ground cooling in winter because branches act as thermal bridges between the cold atmosphere and the warmer ground. This process is particularly active in the Arctic, because frozen wood has a thermal conductivity 50 times larger than Arctic depth hoar. Since shrubs are widespread in the Arctic, thermal bridging must be incorporated in snow models for proper simulations of the ground thermal regime, of the temperature gradient in the snowpack, and of snow metamorphism. In alpine regions, the thermal contrast between wood and snow is less than 10 because unfrozen wood has a lower thermal conductivity than frozen wood and because alpine snow is more conductive than Arctic depth hoar. The thermal impact of mountain shrubs may therefore be considered negligible. Measurements of ground temperature and liquid water content at an Alpine site (Lautaret pass, 2050 m, French Alps) with 2 m tall alders next to mountain grasslands surprisingly show that alders do impact noticeably the ground thermal regime. Under grasslands, the ground remains at 0°C and very little ground water freezes. Under alders, most ground water freezes and the temperature drops below -1°C. We perform finite elements simulations to assess the capacity of the alders to act as thermal bridges though two phenomena: the thickness of the alder branches that compensate the lower wood/snow thermal contrast, and protruding branches acting as radiators releasing heat into the atmosphere. We conclude that shrubs covered by snow affect the ground and the snowpack thermal regime even in alpine regions. The impact of this process on carbon cycling in mountains deserve further investigations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".