Ultra-light photosensor collars to monitor Arctic lemming activity
Bibliographic record
Abstract
Background: Studying the anti-predatory behavior of mammals represents an important challenge, especially for fossorial small mammals that hide in burrows. In the Arctic, such behaviors are critical to the survival of lemmings considering that predation risks are high every summer. Because detailed information about how lemmings use burrows as hideouts is still lacking, we developed a 1.59 g photosensitive collar to record any event of a small mammal moving between a dark area (e.g., burrow) and a bright area (e.g., outside the burrow). Tests of how collars affected lemming behavior were conducted in captivity in Cambridge Bay, Nunavut, Canada, in November 2019 and field tests were conducted on Bylot Island, Nunavut, Canada, in August 2021. Results: The device was made of two chemical batteries and a printed circuit board (PCB) equipped with a photosensor and a real-time clock that recorded amplitude transient thresholds of light (lux) continuously. In accordance with ethical use of such devices, we verified that no abnormal loss of body mass was observed in captive or free-ranging lemmings, and no difference in recapture rates were observed between those with and without a collar, though we could not test this for periods longer than 108 h. Measurements of light intensities revealed consistent patterns with high lux levels at mid-day and lowest during the night. Lemmings showed clearly defined behavioral patterns alternating between periods outside and inside burrows. Despite 24-h daylight in the middle of the summer, August nighttime (i.e., 11 PM to 4 AM) lux levels were insufficient for amplitude transient thresholds to be reached. Conclusion: By taking advantage of the long periods of daylight in the Arctic, such technology is very promising as it sets new bases for passive recording of behavioral parameters and builds on the prospect of further miniaturization of batteries and PCBs.
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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) | 0.000 | 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 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".