Relationships between deer mice and downed wood in managed forests of southern British Columbia
Bibliographic record
Abstract
We examined the relationship between deer mice (Peromyscus maniculatus (Wagner)) and downed wood in a low-elevation Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forest and a high-elevation Engelmann spruce (Picea engelmannii Parry ex Engelm.) subalpine fir (Abies lasiocarpa (Hook.) Nutt.) forest in the south-central interior of British Columbia. We experimentally manipulated the volume of downed wood on clear-cut and forested sites and monitored the response of deer mice with a markrecapture study to assess population densities and survival and reproduction rates. Populations responded positively to harvesting at the low-elevation but not the high-elevation study area. At the low-elevation study area, the population dynamics of deer mice on clear-cut and forested treatments were not positively associated with patterns of vegetation cover or increasing downed-wood volumes. Instead, populations on clearcuts appeared to increase in response to an unknown factor associated with lower volumes. No relationship was detected between population dynamics of deer mice and downed-wood volumes at the high-elevation site. The population dynamics of deer mice on forests at the high-elevation site appeared to be more closely related to vegetation cover than to downed wood. The results indicated that downed wood is not a critical habitat component for deer mice in the south-central interior of British Columbia.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".