A review of factors affecting cave climates for hibernating bats in temperate North America
Bibliographic record
Abstract
The fungal pathogen Geomyces destructans, which causes white-nose syndrome in bats, thrives in the cold and moist conditions found in caves where bats hibernate. To aid managers and researchers address this disease, an updated and accessible review of cave hibernacula and cave microclimates is presented. To maximize energy savings and reduce evaporative water loss during winter, most temperate vespertilionid bats in North America select caves with temperatures between 2 and 10 °C, with 60%–100% relative humidity. Generally, the temperature in caves is similar to the mean annual surface temperature (MAST) of a region, which varies by latitude, altitude, and topography. However, MAST for most areas where caves are found in eastern North America is well above 10 °C. Thus, various factors cause cold-air infiltration that reduces temperatures of these caves during winter. These factors include depth of cave, topographic setting, airflow patterns, cave configuration, and water infiltration. Factors affecting humidity, condensation, and evaporation are also addressed. In areas where MAST is above or below the thermal requirements of Geomyces destructans, many caves used by bats as hibernacula may still provide favorable sites for optimal growth of this fungus.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".