Born to be wild? Response of an urban exploiter to human-modified environment and fluctuating weather conditions
Bibliographic record
Abstract
Human-driven environmental changes affect behavior, morphology, life history, and population dynamics of wild species. Artificial food sources in anthropogenic environments benefit some species and may lead to faster somatic growth and larger body size, which affects survival and reproduction, thus contributing to a species’ success in modified environments. Using raccoons (Procyon lotor (L., 1758)) as a model, we documented age-specific body-mass pattern and evaluated the influence of human activities (human density, area with artificial food sources, edges of forested area bordering corn (Zea mays L.) fields) and weather (index of winter severity and mean annual precipitation) on body-mass variation at multiple spatial scales. The effect of human-driven changes on raccoon mass varied with age, sex, and spatial scale, suggesting that anthropogenic changes affect raccoons differentially according to gender and life stages. Human activity had consistently opposing effects between the sexes. Weather covariates represented >50% of the total variance in body mass explained by our models. Previous winter severity and mean annual precipitation affected body mass negatively and positively, respectively. Our results emphasize the importance of multiscale, sex- and age-specific analyses when studying influences of human activity on wildlife.
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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 | 0.001 |
| 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) | 0.003 | 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".