Habitat selection, movement, and food preferences of Wood Turtles (<i>Glyptemys insculpta</i>) in an agri-forested landscape
Bibliographic record
Abstract
Wood Turtles (Glyptemys insculpta (Le Conte, 1830)) can use agricultural fields for basking and feeding, but hayfields can be an ecological trap due to mortality associated with agricultural machinery. It is unclear if hayfields are selected habitat or simply occur adjacent to used waterways. We sought to investigate Wood Turtle habitat selection at the third- and fourth-order scales in an agri-forested landscape and quantify food abundance (berries, fungi, and gastropods–worms) among habitat types. To quantify habitat selection by Wood Turtles, we radio-tracked 23 adults from May to November of 2018. We measured habitat features at each turtle location and three random sites within 50 m. At the third order, turtles primarily selected for edge habitat and selected hayfields over forest. At the fourth order, turtles selected for low canopy cover and presence of woody debris. Earthworms (suborder Lumbricina) were abundant within hayfields, and berries and fungi were abundant in forests. Turtles abandoned hayfields at the end of July, likely due to the emergence of food within the forest. Food availability likely influences their habitat use during the season, and hayfields provide a food source that entices Wood Turtles during the prime hay harvest period, which likely increases the risk of machinery-related mortality.
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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".