Pallid bat (<i>Antrozous pallidus</i>) foraging over native and vineyard habitats in British Columbia, Canada
Bibliographic record
Abstract
Optimal foraging theory predicts organisms will forage in habitats providing the most profitable prey. Human alterations to ecosystems may affect predators’ foraging activity by changing landscape features, prey types, and prey availability. Assessing the selection of foraging habitats in a heterogeneous landscape can provide data to improve land management and conservation policies. In Canada, the pallid bat ( Antrozous pallidus (LeConte, 1856); Vespertilionidae) is listed as threatened partly because of loss or modification of shrub–steppe habitat. Our purpose was to determine if vineyards provide a suitable surrogate for foraging habitat relative to native habitat. We used pitfall traps to compare prey abundance in each habitat and analyzed faeces to assess diet composition. Over 24 nights, we surveyed both habitats for foraging bats. Bats foraged over vineyards, but we recorded significantly more foraging activity over native habitat. We collected over 2000 arthropods in pitfall traps and found significantly more in native habitat compared with vineyards. Species eaten by pallid bats were present in both habitats. Scarab beetles (Coleoptera: Scarabidae) and Jerusalem crickets (Orthopthera: Stenopelmatidae) represented the principal prey. The use of vineyards by pallid bats for foraging suggests that while they are adapting to a changing landscape, reduced prey abundance in vineyards may negatively affect them over the long term.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".