Meet You at the Local Watering Hole? No Use of an Artificial Water Resource, and Evidence of Dehydration in Hibernating Bats in the Prairies
Bibliographic record
Abstract
(Uploaded by Plazi for the Bat Literature Project) While torpid, small hibernators experience negative water balance due to evaporative water loss. The use of humid hibernacula and ability to drink during periodic arousals allows most hibernators to manage this deficit over the course of a winter. Some populations of big brown bats (Eptesicus fuscus) hibernate in relatively dry rock-crevices that do not contain free standing water. We monitored the winter behaviour and physiology of one such population in the Canadian prairies. Due to the semi-arid climate, we hypothesized that these bats would experience relatively high evaporative water loss and make frequent mid-winter flights to find water. We measured serum ion concentrations and hematocrit to assess level of dehydration in bats captured during winter. We also provided a heated water tank enriched in deuterium (2H) and used stable isotope analysis to test for elevated hydrogen isotope ratios (2H/1H; herein δ2H) in the blood of bats to determine if individuals drank from the tank. We also used passive acoustic monitoring, video surveillance, and passive integrated transponder (PIT) tags to determine if bats visited the heated water tank. We found evidence of hypertonic dehydration (elevated hematocrit and concentrations of some serum ions) in bats as winter progressed. Blood δ2H of bats was similar to that of water on the landscape, and acoustic and video surveillance did not indicate any visits by bats to the water tank. Post-arousal dehydration is not uncommon in hibernators, which agrees with our observation that the water tank did not represent a water resource, despite it being the only open (not frozen) water available. It is unknown whether bats may exploit frozen sources of water (e.g., snow) to supplement metabolic water produced from fat catabolism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".