Impact of variability in measured and simulated tundra snowpack properties on heat transfer metrics
Bibliographic record
Abstract
Tundra snowpack properties are highly heterogenous over a variety of spatial scales and evolve over the course of the winter. Variations in snowpack properties such as snow density and microstructure control the transfer of heat through the snowpack. Thermal properties of the snowpack impact the subnivean environment; snow insulates the underlying soil, allowing films of liquid water to remain unfrozen, enabling biological processes to take place. In this study, field measurements from four field campaigns across two different winters (March and November 2018, January and March 2019) are used to capture and constrain the spatial variability of the snowpack. These include 1050 spatially distributed Snow MicroPenetrometer (SMP) profiles throughout the Trail Valley Creek catchment in the Northwest Territories, Canada. Bespoke coefficients for tundra snowpacks were calculated (based on the work of King et al., 2020) to convert raw SMP force measurements to densities. This allowed density changes of vertical profiles to be assessed and spatial variability in the thickness and properties of three snowpack layers (wind slab, indurated hoar and depth hoar) to be quantified. 105 needleprobe measurements from 37 snowpits were used to contrast the density and thermal conductivity of snowpack layers, as well as thermal conductivities estimated from recalibrated SMP density profiles. These in-situ measurements will be compared to 1-D simulations of snowpack properties from the Community Land Model (PTCLM 5.0) over the two winter seasons. The impact of snowpack layering on snow heat transfer metrics will be investigated using both 2-layer (wind slab: depth hoar) and 3-layer (wind slab: indurated hoar: depth hoar) snowpack configurations. The spatial variability of heat transfer metrics across the Trail Valley Creek catchment will also be considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".