Data from an experimental study to determine the impact of snow hardness on lemming locomotion
Bibliographic record
Abstract
Lemmings are an essential link of the Arctic food web as they are the main prey of many predators inhabiting these regions. These small rodents exhibiting cyclic fluctuation of their population can stay active throughout the winter and must dig a network of tunnels in the snow to reach vegetation on which they are feeding. Therefore, snow hardness where they dig has the potential to affect their effort while digging as well as their performance. Data presented in this publication were obtained from an experimental study conducted in Cambridge Bay (Nunavut) to determine the effect of snow hardness on lemming locomotion. A total of 7 lemmings, 4 brown lemmings (Lemmus trimucronatus) and 3 collared lemmings (Dicrostonyx groenlandicus), were captured and kept in captivity in individual cages in the Canadian High Arctic Research Station (CHARS) from August to November 2019. We collected snow samples in 2 observation boxes with windows on the side (100 x 31 x 8 cm, length x height x width) allowing us to observe the lemmings while digging in the snow. The snow collected was categorized in 3 main types: soft, hard, rain-on-snow (ROS). Measurements of snow physical properties (density, hardness) were taken in all recognizable snow layers, from top to bottom: ros-A (only for ROS snow type), A, B, C (see Lemming locomotion codes). Each trial consisted in introducing a lemming on top of a snow sample in the observation box and filming its behavior for 30 minutes. Each lemming (n = 7) was tested 3 times on each snow type (soft, hard, ROS) for a total of 63 trials. Then, the 63 videos were processed to identify the time spent by the lemmings performing different behaviors during the trials. The total length of tunnels dug during each trial were also measured, as well as the time spend above and inside the snow, the time spent using their incisors and the time spent in every snow layer. Digging speeds were obtained in the different layers of the different types of snow. For further details regarding the methods, please refer to the related publication of Poirier et al. (2021).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".