Food subsidies of raccoons (<i>Procyon lotor</i>) in anthropogenic landscapes
Bibliographic record
Abstract
Food subsidies from human sources are often exploited by free-ranging vertebrates living in human-dominated landscapes. To explore the importance and attempt to estimate the reliance of raccoons (Procyon lotor (Linnaeus, 1758)) — common synanthropes in North America — on such food subsidies, we analyzed hair samples from 122 raccoons collected across four states in the Midwestern United States (Wisconsin, Minnesota, Iowa, and Illinois), including 9 raccoons that were livetrapped and sampled in Madison (Wisconsin). We found that raccoons inhabiting areas with more agriculture had higher δ13C values, indicating a diet enriched with anthropogenic food from C4 photosynthetic plants, like corn (Zea mays L.). Surprisingly, raccoons inhabiting increasingly urban areas showed lower δ13C values, suggesting a diet with less anthropogenic food. Regardless, raccoons in urban areas enriched in 13C possessed high indices of body condition, suggesting that anthropogenic food subsidies are contributing to their overall nutritional condition. Our findings reveal that the degree to which synanthropes rely upon human foods differs by land-cover type and that the use of these calorically rich subsidies has important implications on individual health.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".