Using multiple abundance estimators to infer population trends in Atlantic Puffins
Bibliographic record
Abstract
We used five techniques to estimate the number of Atlantic Puffins (Fratercula arctica) using a study plot on Gull Island, Newfoundland, during the 2000 breeding season. Grubbing of burrows yielded an estimate of a breeding population of 522 (95% CI: 364668) Atlantic Puffins on this plot. Attendance counts of birds standing on the plot consistently underestimated the breeding population. A closed-population estimator with sighting heterogeneity estimated that of the 535 Atlantic Puffins banded since 1997, 370 (336404) used the plot in 2000. Using 370 birds as the marked population, a corrected LincolnPetersen index estimated a total population of 1712 (12332191) based on captured birds. Based on resights of birds, ratios of banded birds to total attendance estimated 2927 (26083335), and the Bowden estimator gave 3502 (30543950) Atlantic puffins. We projected an age-based matrix using literature values, and extracted the proportions of nonbreeding birds and young birds expected at a stable age distribution and compared the proportions with observed values. Based on the large number of nonbreeders suggested by the abundance estimates, we suspect that this population (i) is stable or increasing, (ii) includes breeding-age Atlantic Puffins that do not breed, and (iii) has been enjoying high fecundity in recent years.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".