Using wingbeat frequency to estimate mass gained by seabirds
Bibliographic record
Abstract
Abstract 1. Energy intake is a fundamental currency in ecology that is critical to reproductive success, survival and lifetime fitness. Measuring foraging success in wild animals via biologgers has been a long-standing challenge. 2. Flying animals gain mass during foraging, and they must counteract the associated increased gravitational force by creating additional lift. Pennycuick (1996) proposed that wingbeat frequency ( w ) should vary with the square root of body mass , when other variables influencing wingbeat frequency are held constant. 3. We present a state-space model that estimates instantaneous changes in body mass by modelling this relationship with wingbeat frequency using animal-borne accelerometer and depth data. To demonstrate the usefulness of our proposed method, we applied it to biologging data from 55 thick-billed murres ( Uria lomvia ) during the incubation period. 4. Our mass estimates allowed us to identify areas associated with higher gains, and to demonstrate that foraging success was generally higher farther from the colony. However, 79% of foraging trips were associated with mass deficits. We performed simulation studies to assess the sensitivity of our method to parameter misspecification and the increase in accuracy gained from including the known mass at recapture. As estimates of energy intake allow for testing of long-standing hypotheses in foraging ecology, our method provides a new tool to help answer these questions with any animal that engages in flapping flight.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".