Bibliographic record
Abstract
Abstract Rapid acceleration is the key to a successful escape manoeuvre and has attracted considerable research attention in a wide array of taxa. I recorded take‐offs of least auklets Aethia pusilla and crested auklets Aethia cristatella with digital video (60 frames per second). To smooth time–location data derived from video, I used predicted mean square error quintic splines, which have been shown to be good predictors of true acceleration. Repeated recordings of the same individual bird allowed me to measure repeatability of take‐off acceleration and velocity to find the most robust and biologically meaningful measure. The most repeatable take‐off parameters were power at time t=0.17 s after take‐off (r=75%) and acceleration at t=0.17 s (r=72%). The horizontal component of velocity at t=0.32 s was least affected by the slope of the take‐off trajectory. The mean acceleration of both species is close to expected values based on body mass, even though all previously studied species had considerably lower body mass. Within least auklets, however, I did not find a significant relationship of velocity or acceleration with mass. This would be expected if the observed drop in mass after hatching was an adaptation to reduce the risk of predation. I conclude that acceleration and exerted power at a certain time after take‐off is repeatable and the most suitable measure of performance for both inter‐ and intra‐specific comparisons.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".