Bibliographic record
Abstract
Becoming a parent can be the most rewarding yet arduous experience. Incubating king penguins have to tough it out on sub-Antarctic beaches, taking it in turns to venture off in search of food while waiting for the egg to hatch. But at what cost? How much energy do the stay-at-home parents consume? René Groscolas and his colleagues from the Institut Pluridisciplinaire Hubert Curien, France, and the Université Laval, Canada, explain that scientists usually estimate energy expenditure by simply measuring a bird's heart rate. However, the team suspect that the act of measuring birds' oxygen consumption as a function of their heart rate could have flustered the animals and sent their heart rates rocketing, causing scientists to underestimate energy consumption rates (p. 153).The team decided to remeasure incubating king penguins' energy consumption rates without agitating the birds. Knowing that king penguins lose weight while incubating their individual eggs, the team recorded the incubating birds' heart rates, measured their weight loss and converted the weight loss into energy consumed. Plotting the birds heart rates against their energy consumption, Groscolas and his colleagues found that the birds were using as much as 26% more energy than had been estimated previously. ‘This result suggests that stress induces a disproportionate increase of heart rate versus oxygen consumption and that the use of energy expenditure/heart rate relationships obtained in stressed birds could lead to underestimated energy consumption values,’ say Groscolas and his colleagues.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".