Estimating the influence of the thermal environment on activity patterns of the desert woodrat (<i>Neotoma lepida</i>) using temperature chronologies
Bibliographic record
Abstract
Environmental temperature influences the ecology and life history of animals. In habitats near the thermal range boundary, fluctuations in temperature may influence the ability of species to persist. Desert woodrats ( Neotoma lepida Thomas, 1893) occupy one of the hottest and most extreme environments in the western hemisphere, Death Valley, California, despite limited adaptations for water conservation or efficient heat dissipation. Moreover, N. lepida have a relatively low tolerance for high temperature. Thus, we hypothesized temperature might influence both the timing and the duration of activity. To test this idea, we attached iButton sensors to 56 animals over a 2-year period and recorded activity. Each sensor was set to record at 5 or 15 min intervals and stored approximately 2000 records before retrieval. We found a strong relationship between ambient temperature and onset and duration of activity, influenced by both body size and gender. Neotoma lepida did not emerge until air temperature fell below 42 °C. As daily high temperatures increased, both sexes had fewer nightly activity bouts of shorter duration. Our results suggest that activity of N. lepida is constrained during the climatically intense summer months. Animals face a trade-off between remaining in the thermal safety of the den vs. emerging to obtain resources.
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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 | 0.000 |
| 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".