Effect of age on energy storage during prehibernation swarming in little brown bats (Myotis lucifugus)
Bibliographic record
Abstract
At temperate latitudes insectivorous bats face substantial nutritional demands prior to hibernation. As temperature decreases and availability of insect prey declines, bats must deposit nutrient stores for hibernation. The use of torpor allows bats to limit energy expenditures resulting in a net energy gain despite decreased energy intake. However, subadult bats have lower initial fat stores than adults and may have greater difficulty depositing sufficient nutrient stores to survive the winter. We used plasma metabolite analysis to determine the fueling performance of little brown bats ( Myotis lucifugus (LeConte, 1831)) during swarming to see if subadults compensated for the increased challenges by increased feeding throughout the swarming period. During the period of our study (August and September), adult bats gained mass, while subadults lost mass. There was, however, no difference in nutrient intake of the age groups as indicated by plasma metabolite concentrations. The number of bats using torpor while roosting in the hibernaculum by day increased exponentially coincident with the onset of mating and a decrease in nutrient intake. The results are consistent with wild bats using torpor to minimize energy expenditure and compensate for lower nutrient intake. The difference in mass change for adults and subadults despite the same nutrient intake indicates that subadults incur greater energetic costs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".