Using subnivean camera traps to study Arctic small mammal community dynamics during winter
Bibliographic record
Abstract
Small rodents are a key indicator to understand the effect of rapidly changing winter climate on Arctic tundra ecosystems. However, monitoring rodent populations through the long Arctic winter by means of conventional traps has, until now, been hampered by snow cover and harsh ambient conditions. Here, we conduct the first extensive assessment of the utility of a newly developed camera trap to study the winter dynamics of small mammals in the Low Arctic tundra of northern Norway. Forty functional cameras were motion-triggered 20 172 times between September 2014 and July 2015, mainly by grey-sided voles (Myodes rufocanus (Sundevall, 1846)), tundra voles (Microtus oeconomus (Pallas, 1776)), Norwegian lemmings (Lemmus lemmus (Linnaeus, 1758)) and shrews (Sorex spp.). These data proved to be suitable for dynamical modelling of species-specific site occupancy rates. The occupancy rates of all recorded species declined sharply and synchronously at the onset of the winter. This decline happened concurrently with changes in the ambient conditions recorded by time-lapse images of snow and water. Our study demonstrates the potential of subnivean camera traps for elucidating novel aspects of year-round dynamics of Arctic small mammal communities.
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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 | 0.001 | 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) | 0.000 | 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".