Little brown Myotis roosts are spatially associated with foraging resources on Prince Edward Island
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) Animal fitness is closely linked to accessing and capitalizing on local resources such as prey and shelter. Resources vary in quality, thus individuals may demonstrate selectivity for particular resource types. We examined resource selection in roost selection patterns of temperate bats on Prince Edward Island. To complement existing work examining roost structural characteristics, we evaluated whether roost selection by little brown myotis (Myotis lucifugus) could be explained by landscape characteristics. Given a sample of roosts identified through radio telemetry, community reports and a randomly selected sample of comparison structures, we determined that a combination of proximity to forest and open wetland best explained roost selection. Roost selection appears to reflect the optimization of time and energy budgets, and the proportion of maternity roosts within the sample suggests that these constraints may be more acute in reproductive females. Given the importance of roosts for reproductive success in females, future work should seek to quantify the role of physical characteristics on roost structure selection and the preservation of suitable roosting 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".