Plunge-diving into dynamic body acceleration and energy expenditure in the Peruvian booby
Bibliographic record
Abstract
Daily energy expenditure (DEE) is the result of decisions on how to allocate time among activities (resting, commuting and foraging) and the energy costs of those activities. Dynamic body acceleration (DBA), which measures acceleration associated with movement, can be used to estimate DEE. Previous studies of DBA-DEE correlations in birds were carried out on species foraging below their thermoneutral zone, potentially decoupling the DBA-DEE relationship. We used doubly labelled water (DLW) to validate the use of DBA on plunge-diving seabirds, Peruvian boobies (Sula variegata), foraging in waters above their thermoneutral zone (>19°C). Mass-specific DEEDLW in boobies was 1.12 kJ day-1 g-1, and higher in males than in females. DBA alone provided the best fitting model to estimate mass-specific DEEDLW compared with models partitioned per activity and time budget models. Nonetheless, the model parametrizing activity at and away from their onshore breeding colony was the most parsimonious (r=0.6). This r value, although high, is lower than that of all other avian studies, implying that temperature is not the main cause of DBA-DEE decoupling in birds. Time at the colony (∼80% of the day) was the largest contributor to DEE as it was the most time-consuming activity and involved nest defence. However, foraging was the most power-consuming activity (4.6 times higher activity-specific metabolic rate than resting at the colony), and commuting flight was higher than in other gliding seabirds. In short, DBA alone can act as a proxy for DEE, opening avenues to measure the conservation energetics of this seabird in the rapidly changing Peruvian Humboldt Current system.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".