Population dynamics of hispid cotton rats (<i>Sigmodon hispidus</i>) across a nitrogen-amended landscape
Bibliographic record
Abstract
We conducted a markrecapture experiment to examine the population dynamics of hispid cotton rats (Sigmodon hispidus) in response to low-level nitrogen amendments (16.4 kg nitrogen/ha per year) and exclosure fencing in an old-field grassland. The experimental design consisted of sixteen 0.16-ha plots with 4 replicates of each treatment combination. We predicted that densities, reproductive success, movement probabilities, and survival rates of cotton rats would be greater on nitrogen-amended plots because of greater aboveground biomass and canopy cover. Population densities of cotton rats tended to be highest on fenced nitrogen plots, but densities on unfenced nitrogen plots were similar to those on control and fenced plots. We observed no distinct patterns in survival rates, reproductive success, or movement probabilities with regard to nitrogen treatments. However, survival rates and reproductive success tended to be higher for cotton rats on fenced plots than for those on unfenced plots and this was likely attributable to decreased predation on fenced plots. As low-level nitrogen amendments continue to be applied, we predict that survival, reproduction, and population-growth rates of cotton rats on control plots, especially fenced plots with no nitrogen amendment, will eventually exceed those on nitrogen-amended plots as a result of higher plant-species diversity, greater food availability, and better quality cover.
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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 | 0.000 | 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) | 0.000 | 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".