A novel technique to measure chronic levels of corticosterone in turtles living around a major roadway
Bibliographic record
Abstract
Conservation biology integrates multiple disciplines to expand the ability to identify threats to populations and develop mitigation for these threats. Road ecology is a branch of conservation biology that examines interactions between wildlife and roadways. Although the direct threats of road mortality and habitat fragmentation posed by roads have received much attention, a clear understanding of the indirect physiological effects of roads on wildlife is lacking. Chronic physiological stress can lower immune function, affect reproductive rates and reduce life expectancy; thus, it has the potential to induce long-lasting effects on populations. Reptiles are globally in decline, and roads are known to have negative effects on reptile populations; however, it is unknown whether individual responses to roads and traffic result in chronic stress that creates an additional threat to population viability. We successfully extracted reliable measures of corticosterone (CORT), a known, commonly used biomarker for physiological stress, from claw trimmings from painted turtles (Chrysemys picta) captured at three study sites (road-impacted site, control site and validation site). Corticosterone levels in claws were evaluated as a measure of chronic stress in turtles because CORT is deposited during growth of the claw and could provide an opportunity to examine past long-term stress levels. While male turtles had higher CORT levels on average than females, there was no difference in the level of CORT between the road-impacted and control site, nor was there a relationship between CORT and turtle body condition. In validating a novel approach for non-invasive measurement of long-term CORT levels in a keratinized tissue in wild reptiles, our study provides a new avenue for research in the field of stress physiology.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".