Chemical, thermal, and physical properties of sites selected for overwintering by northern wood turtles (<i>Glyptemys insculpta</i>)
Bibliographic record
Abstract
Northern ectotherms must seek refuge from winter conditions for a large portion of their annual activity cycle. The objective of this study was to quantify physical properties of overwintering sites selected by wood turtles ( Glyptemys insculpta (LaConte, 1830)) at the species’ northern range limit. We mapped all structural features (e.g., root balls and log jams), water depth, and sediment types along a 1.5 km stretch of river that was available to turtles outfitted with radio transmitters (N = 8) during winter. Temperature selection was assessed by comparing thermal profiles from data loggers on turtles and temperature stations within the river and other riparian habitats (e.g., ephemeral pools and oxbows). Dissolved oxygen (DO) was measured at each temperature station and turtle location. Wood turtles overwintered in the river, which was colder (~0 °C), had more stable temperatures, and provided higher DO (12.64 ppm) compared with adjacent habitats. Some turtles selected structured refuges for overwintering. Winter movements were not related to temperature or DO, but may be related to maintaining a certain distance from shore and water depth to protect against accidental relocations during winter. We discuss hibernacula as potential factors limiting the northern distribution of wood turtles, a species at risk in Canada.
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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".