Food and cover resources for small mammals on an industrially logged landscape in the Sierra Nevada of California
Bibliographic record
Abstract
The presence and abundance of organisms within an ecosystem often correlate with habitat variables that may have few, or unknown, functional values. Understanding the functional role of these variables is especially important for organisms occupying landscapes managed for timber production and containing diverse habitat patches of different quantities and structures of vegetation. We investigated the strength of associations reported in the literature between small mammal generalists and vegetation. On an industrially logged landscape in northern CA, we used occupancy and mark-recapture analyses across three years to estimate the presence and total numbers for woodrats ( Neotoma fuscipes Baird, 1858) and deer mice ( Peromyscus maniculatus (Wagner, 1845)) related to vegetative attributes on coniferous and non-coniferous sites. Abundances of the small mammals correlated positively with shrub cover and hardwoods for woodrats and with shrubs and masting species for deer mice. Indices describing the value of vegetation features for both food and cover, but not these resources independently, described both species’ presence and abundances best. We demonstrated that shrubs and non-coniferous trees are particularly important for small mammals and should be of particular focus to forest managers in the Sierras and mountains with similar forest structures.
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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 | 0.001 | 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".