Structure, specific surface area and thermal conductivity of the snowpack around Barrow, Alaska
Bibliographic record
Abstract
The structure of the snowpack near Barrow was studied in March–April 2009. Vertical profiles of density, specific surface area (SSA) and thermal conductivity were measured on tundra, lakes and landfast ice. The average thickness was 41 cm on tundra and 21 cm on fast ice. Layers observed were diamond dust or recent wind drifts on top, overlaying wind slabs, occasional faceted crystals and melt‐freeze crusts, and basal depth hoar layers. The top layer had a SSA between 45 and 224 m 2 kg −1 . All layers at Barrow had SSAs higher than at many other places because of the geographical and climatic characteristics of Barrow. In particular, a given snow layer was remobilized several times by frequent winds, which resulted in SSA increases each time. The average snow area index (SAI, the dimensionless vertically integrated SSA) on tundra was 3260, higher than in the Canadian High Arctic or in the Alaskan taiga. This high SAI, combined with low snow temperatures, imply that the Barrow snowpack efficiently traps persistent organic pollutants, as illustrated with simple calculations for PCB 28 and PCB 180. The average thermal conductivity was 0.21 Wm −1 K −1 , and the average thermal resistance on tundra was 3.25 m 2 K W −1 . This low value partly explains why the snow‐ground interface was cold, around −19°C. The high SAI and low thermal resistance values illustrate the interplay between climate, snow physical properties, and their potential impact on atmospheric chemistry, and the need to describe these relationships in models of polar climate and atmospheric chemistry, especially in a climate change context.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".