Roost site selection by forest-dwelling male Myotis in central Ontario, Canada
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) We used radiotelemetry and random exit counts to determine roost site selection by male northern long-eared bats (Myotis septentrionalis) and unidentified Myotis, either northern long-eared or little brown bats (M. lucifugus) in a conifer-dominated, mixedwood forest landscape in central Ontario, Canada. We compared the characteristics of snags used as roosts (n = 26), with randomly located snags and random points (n = 52 and 50, respectively), at three spatial scales: focal tree, surrounding forest, and landscape. Snags used as roost sites by these bats differed from random snags for 19 of the 23 variables measured (P < 0.05). Logistic regression models were derived which suggested that the bats selected large snags, in open canopies, of intermediate stages of decay, that were located in upland areas, but away from water bodies. Bats may have selected roost sites for their thermal advantage in the mornings, as most used cavities or bark on the south and east sides of snags. To ensure that snags of appropriate characteristics persist for these two species of Myotis, forest managers in the northern Great Lakes forest region should retain, and manage for, large white pine (Pinus strobus) and trembling aspen (Populus tremuloides) snags at sites in forests that meet these criteria. Such a regime is possible under a selection harvest that is normally practiced in these mixedwood forest types.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".