Five months under ice: biologging reveals behaviour patterns of overwintering freshwater turtles
Bibliographic record
Abstract
Winter in temperate regions is characteristically the coldest period of the year. Species in these regions adapt to freezing temperatures with physiological or behavioural mechanisms to mitigate the threats of cold exposure. For aquatic species, taking refuge under the ice minimizes the risk of experiencing freeze injury. The northern map turtle ( Graptemys geographica (LeSueur, 1817)) is one species that overwinters under the ice of lakes and rivers. Here, we observed the behaviour of free-ranging map turtles at a known overwintering site throughout an entire winter using biologgers equipped with tri-axial acceleration, temperature, and depth sensors. We observed that map turtles maintain localized locomotor activity at the overwintering site continuously during the winter. The extent and patterns of locomotor activity and habitat use varied between adult females, adult males, and juvenile females. Adult females were observed at the shallowest depths, coldest temperatures and moved the least, whereas juvenile females were observed at the deepest depths, warmest temperatures and moved the most. All groups remained at temperatures near freezing (0.98–1.39 °C) and at average depths ranging from 1.34 to 1.7 m. These behavioural patterns are consistent with a strategy to survive the winter while remaining aerobic and likely reflect differences in physiological demands.
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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) | 0.001 | 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".