Using Thermodynamics to Improve Bat Houses in Cold Climates
Bibliographic record
Abstract
Abstract Wildlife managers design artificial structures, such as bird and bat houses, to provide alternative habitats that aid wildlife conservation. However, prototypes may not be equally efficient at all sites due to varying climate or habitat characteristics influencing thermal properties. For example, bat houses are a popular measure employed to protect bats and educate citizens, yet bat houses have achieved limited success in cool climates. To address this problem, we tested different orientations and mountings for both traditional and newly designed bat house models based on modern architectural energy saving concepts, by recording temperatures in bat houses across a climate gradient in Quebec, Canada. Bat houses mounted on buildings had warmer conditions at night than those on poles and warmed sooner in the morning when facing east. Our new insulated model with passive heating maximized the time in the extended optimal temperature range (22 − 40 °C) of targeted species by up to 13% compared to the Classic model, providing bats with an estimated average daily energy savings of up to 7.8% when mounted on a building. We conclude that the use of energy-saving concepts from architecture can improve the thermal performance of wildlife 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".