Are salamanders good bioindicators of sustainable forest management in boreal forests?
Bibliographic record
Abstract
Salamanders have been identified as potential indicators of sustainable forest management in boreal Ontario, Canada. However, little information is available on their distribution, abundance, and habitat associations within the boreal forests on which to base a monitoring program. We surveyed salamanders near White River, Ontario, and related their distribution to climate and vegetation information and to habitat suitability models currently used for forest planning within the region. Primarily red-backed salamanders ( Plethodon cinereus Green) and blue-spotted salamanders ( Ambystoma laterale Hallowell) were recorded, although both were observed in low numbers and captures varied spatially and temporally. Capture rates were 3–7 times lower for P. cinereus than has been reported elsewhere. Trend monitoring will be expensive and have low power to detect significant declines over moderate time frames unless capture rates can be doubled and within-site variability in capture rates halved. We found few strong habitat relationships using either coverboard or pitfall trap data. Plethodon cinereus was negatively correlated with the volume of downed wood, which has been noted in other regions and may be an artefact of the coverboard survey technique. Further focused studies in the boreal forest are required to support the use of both habitat supply models and trend analysis to monitor salamander populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".