Deer mouse demography in burned and unburned forest: no evidence for source–sink dynamics
Bibliographic record
Abstract
Deer mouse ( Peromyscus maniculatus (Wagner, 1845)) populations increase dramatically after wildfires. These increases are puzzling because there are no obvious food sources or vegetation cover in severely burned areas. We conducted a capture–mark–recapture study of deer mice in a mosaic of burned and unburned montane forests in western Montana to determine if their postfire increase could be explained by source–sink dynamics, with burned areas acting as a sink. When overall mouse densities were very low, the vast majority of the population was found in burned areas. Mice appeared regularly in unburned forest only when the densities were high. This pattern is precisely opposite to the expected results if the sink hypothesis were correct. Moreover, mice in burned areas did not show decreased body mass, reproductive performance, or survival when compared with mice in unburned areas. Age structure and sex ratio did not differ between burned and unburned sites. We conclude that burned areas do not function as population sinks; rather, they represent high-quality habitat for deer mice.
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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 | 0.001 | 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) | 0.001 | 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".