Out of the Woods: Mitigating Negative Impacts of Unused Forest Roads on Amphibians with Woody Debris
Bibliographic record
Abstract
Out of the Woods: Mitigating Negative Impacts of Unused Forest Roads on Amphibians with Woody Debris Habitat loss and fragmentation are among the most serious threats facing amphibians. While less noticeable than highways and with typically little vehicle traffic, extensive networks of logging roads also fragment habitats and some species avoid crossing these roads. Woody debris is an important habitat feature for many amphibians providing refuges and foraging opportunities for species sheltering underneath. In an attempt to mitigate the negative impacts of habitat fragmentation by logging roads in Algonquin Provincial Park, Canada, we sampled amphibians crossing an unused logging road using pitfall traps and tested several types of woody debris treatments. Using the ‘before’ and ‘after’, ‘control’ and ‘impact’ (BACI) model, we compared captures of Red-backed Salamanders (Plethodon cinereus), Red Efts (Notophthalmus viridescens) and Green Frogs (Lithobates clamitans) in 2010 (pre-treatment) and 2011 (post-treatment); the treatments consisted of either hardwood mulch, conifer brush, timbers and a control with no woody debris. Our results with pitfall traps indicated specific responses to mitigation with Green Frogs showing positive responses to the brush and timber treatments, while both salamander species showed little response.
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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 | 0.001 | 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".