Bibliographic record
Abstract
Getting hot is a natural side effect of exercise and many animals have evolved strategies for dumping excess heat, from panting to sweating. But losing heat is particularly challenging for birds. Wrapped in a coat of insulating feathers, they can record some of the highest metabolic rates measured and generate colossal amounts of heat when they fly, which can put them at risk of overheating. However, Kyle Elliott from McGill University, Canada, explains that some birds are able to dissipate heat through their beaks. ‘Bills have lots of blood vessels but no insulation, so they can shunt extra heat away from the body’, says Elliott. Knowing that the largest members of the puffin family, tufted puffins (Fratercula cirrhata) are likely to have to work harder than most to remain airborne, Elliot wondered if the birds’ relatively large beaks might be acting like radiators to keep their temperature down after they return from a flight.To test the theory, Elliot and graduate student Shannon Whelan needed a way of recording the birds’ beak and body temperatures when they returned to land from foraging trips. Fortunately, at the time, Whelan's partner, Hannes Schraft, was taking thermal shots of rattlesnakes with an infrared camera at San Diego State University and University of California, Davis, USA, so he joined Elliot and Whelan on Middleton Island, Alaska, with the camera to capture the birds' returns.Taking thermal images every 2 min after the puffins landed, the team saw the birds’ beaks cool from ∼25°C to ∼20°C over 30 min; in contrast, their temperature on the outside of their feathers barely changed at all. And, when they calculated how much heat was radiating from the birds’ beaks, it was almost 20% of the total over the animal's whole body, even though the beak makes up only 6% of the bird's surface area.Schraft suspects that dumping heat from the beak could allow the birds to sustain the massive energy outputs that are necessary to keep them aloft. Whelan adds, ‘High energy expenditure during flight might have led to the evolution of large bills in puffins even though they should have small bills [to conserve heat] given the cold climates that they live in’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".