Bibliographic record
Abstract
1. Arctic animals inhabit some of the coldest environments on the planet and have evolved physiological mechanisms for minimizing heat loss under extreme cold. However, the Arctic is warming faster than the global average and how well Arctic animals tolerate even moderately high air temperatures (Ta) is unknown. 2. Using flow-through respirometry we investigated the heat tolerance and evaporative cooling capacity of snow buntings (Plectrophenax nivalis; ≈ 31g, N = 42), a cold specialist, Arctic songbird. We exposed buntings to increasing Ta and measured body temperature (Tb), resting metabolic rate (RMR), rates of evaporative water loss (EWL) and evaporative cooling efficiency (the ratio of evaporative heat loss to metabolic heat production). 3. Buntings had an average (±SD) Tb of 41.3 ± 0.2 °C at thermoneutral Ta, and increased Tb to a maximum of 43.5 ± 0.3 °C. Buntings started panting at Ta of 33.2 ± 1.7 °C, with rapid increases in EWL starting at Ta = 34.6 °C, meaning they experienced heat stress when air temperatures were well below their body temperature. Maximum rates of EWL were only 2.9x baseline rates at thermoneutral Ta, a markedly lower increase than seen in more heat tolerant arid-zone species (e.g., ≥ 4.7x baseline rates). Heat stressed buntings also had low evaporative cooling efficiencies, with 95% of individuals unable to evaporatively dissipate an amount of heat equivalent to their own metabolic heat production. 4. Our results suggest that buntings’ well-developed cold tolerance may come at the cost of reduced heat tolerance. As the Arctic warms, and this and other species experience increased periods of heat stress, a limited capacity for evaporative cooling may force birds to increasingly rely on behavioural thermoregulation, such as minimizing activity, at the expense of diminished performance or reproductive investment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".