Automatic monitoring of the effective thermal conductivity of snow in a low-Arctic shrub tundra
Bibliographic record
Abstract
Abstract. The effective thermal conductivity of snow, keff, is a critical variable which determines the temperature gradient in the snowpack and heat exchanges between the ground and the atmosphere through the snow. Its accurate knowledge is therefore required to simulate snow metamorphism, the ground thermal regime, permafrost stability, nutrient recycling and vegetation growth. Yet, few data are available on the seasonal evolution of snow thermal conductivity in the Arctic. We have deployed heated needle probes on low-Arctic shrub tundra near Umiujaq, Quebec, (N56°34'; W76°29') and monitored automatically the evolution of keff for two consecutive winters, 2012–2013 and 2013–2014, at four heights in the snowpack. Shrubs are 20 cm high dwarf birch. Here, we develop an algorithm for the automatic determination of keff from the heating curves and obtain 404 keff values. We evaluate possible errors and biases associated with the use of the heated needles. The time evolution of keff is very different for both winters. This is explained by comparing the meteorological conditions in both winters, which induced different conditions for snow metamorphism. In particular, important melting events in the second year increased snow hardness, impeding subsequent densification and increase in thermal conductivity. We conclude that shrubs have very important impacts on snow physical evolution: (1) shrubs absorb light and facilitate snow melt under intense radiation; (2) the dense twig network of dwarf birch prevent snow compaction, and therefore keff increase; (3) the low density depth hoar that forms within shrubs collapsed in late winter, leaving a void that was not filled by snow.
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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 | 0.000 | 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) | 0.001 | 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".