Dealing with the heat: Assessing heat stress in an Arctic seabird using 3D-printed thermal models
Bibliographic record
Abstract
The Arctic is warming at four times the global average rate and most studies have focused on the indirect (e.g., changes in food web) rather than the direct effects of climate change. However, as Arctic animals often have low capacity to dissipate heat, the direct effect of warming could impact them significantly (heat stress). To study heat stress, biophysical models have been used in many species to estimate operative temperature (Te, integrated temperature of the thermal environment experienced by an individual). Here, we developed biophysical models of an Arctic seabird, the thick-billed murre (Uria lomvia). We demonstrated that 3D-printed painted models perform similarly to the more traditionally used feather-covered models. We deployed our models on Coats Island, Nunavut, Canada to study heat stress, which occurs in murres when operative temperature is above 21.2 °C (the temperature at which evaporative water loss (EWL) rates increase to maintain a constant body temperatures). Murre operative temperatures ranged from 5.5 °C to 46.5 °C despite ambient temperatures never exceeding 24.7 °C (range: 3.4-24.7 °C), and murres experienced heat stress on 61 % of the days during the breeding season (range: 24-85 %). Using known equations of EWL as a function of temperature, we estimated that murres lost 3.79 % to 4.61 % of their body mass in water daily. Our study confirms the physiological challenges faced by Arctic seabirds during the breeding season, while also demonstrating the value of biophysical models as non-invasive tools to study the effects of heat stress on seabirds.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".