Diet: it’s all about location, location, location! How urbanization influences endocrine stress, isotopic signatures, and fecal antioxidants in eastern chipmunks (Tamias striatus)
Bibliographic record
Abstract
As cities continue to expand on a global scale, animals gain greater access to human food \nwaste, and the consequences associated with the consumption this food waste are poorly \nunderstood. Using eastern chipmunks (Tamias striatus) as a study species, I examined the \ndifferences in cortisol concentrations and body condition scores, as well isotopic signatures of \ncarbon (∂13C) and nitrogen (∂15N), and fecal antioxidants from eastern chipmunks on an \nurbanization gradient. I tested the hypotheses that: 1) chipmunks would have lower cortisol \nlevels and better body condition scores if they are living in more urban areas because urban \nenvironments may contain higher amounts of human food waste relative to natural habitats, and \n2) chipmunks would have higher carbon and nitrogen signatures and excrete more antioxidants \nin more urban environments compared to their natural counterparts because they may be \nconsuming human food waste. Chipmunks were sampled across Sudbury, Ontario from 20 areas \nwith varying levels of urbanization. Each time a chipmunk was captured, hair samples, fecal \nsamples, and body measurements were collected. To quantify urbanization, I surveyed all study \nsites over a three-day period to score the level of human activity. I found that cortisol \nsignificantly differed among chipmunks across the gradient, such that chipmunks in more urban \nhabitats experienced the highest levels of cortisol. I found chipmunks and poorer body condition \nin the most urban areas. Chipmunks in more urban habitats produced a higher nitrogen signature \nthan their natural counterparts, while no significant difference was observed in carbon or fecal \nantioxidants. My results help us to understand differences in diet across as well as the \nphysiological changes associated with urban habitats, which may help us understand how other \nspecies may respond to urbanization
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".