Not just any old pile of dirt: evaluating the use of artificial nesting mounds as conservation tools for freshwater turtles
Bibliographic record
Abstract
Abstract The viability of freshwater turtle populations is largely dependent on the survivorship of reproducing females but females are frequently killed on roads as they move to nesting sites. Installing artificial nesting mounds may increase recruitment and decrease the risk of mortality for gravid females by enticing them to nest closer to aquatic habitats. We evaluated the effectiveness of artificial nesting mounds installed in Algonquin Park, Canada. Artificial mounds were monitored for 2 years to determine if turtles would select them for nest sites. We also simulated turtle paths from wetlands to nests to determine the probability that females would encounter the new habitat. A transplant experiment with clutches ofChrysemys pictaandChelydra serpentinaeggs compared nest success and incubation conditions in the absence of predation between artificial mounds and natural sites. More turtles than expected used the artificial mounds, although mounds comprised a small proportion of the available nesting habitat and the simulations predicted that the probability of females encountering mounds was low. Hatching success was higher in nests transplanted to artificial mounds (93%) than in natural nests (56%), despite no differences in heat units. Greater use than expected, high hatching success, and healthy hatchlings emerging from nests in artificial mounds suggest promise for their use as conservation tools.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".