Investigation of cues used by predators to detect Snapping Turtle (<i>Chelydra</i> <i>serpentina</i>) nests
Bibliographic record
Abstract
Nest predation is the leading cause of reproductive mortality in oviparous tetrapods and can limit population growth in some species. Rates of nest predation could be influenced through modification of the cues used to find nests, but this requires a clear understanding of how nests are located. Here, we used a buffet-style choice experiment to test the relative role of three cue types (visual, tactile, and chemosensory) on the detection and depredation of Snapping Turtle (Chelydra serpentina (L., 1758)) nests by a suite of predators dominated by raccoons (Procyon lotor (L., 1758)). We created sets of artificial nests along an authentic nesting site, presenting single or multiple cues. We interspersed artificial nests with authentic nests and monitored predation rates on both. Predators used all three cues to locate potential nests for investigation. However, nests with tactile cues were significantly more likely to be depredated than nests with only visual and chemosensory cues. Multiple cues had additive effects on predation probability. Addition of chemosensory cues to tactile treatments increased the probability of predation. The importance of tactile cues in this system supports the use of nest cages to protect nests in early stages of development, but cannot explain the recently described late-stage peak in predation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".