Estimating breeding status in Atlantic puffin colonies across Newfoundland: \na methodological comparison
Bibliographic record
Abstract
The largest colonies of Atlantic puffins (Fratercula arctica) have been experiencing decades of declining population growth linked to poor breeding performance, particularly in the Eastern Atlantic. These trends have been revealed by the presence of colony-specific monitoring programs. Such data are fragmented and not updated for Newfoundland (Canada) colonies, the largest in the Western Atlantic. Here, I have assessed the burrow laying success, fledging success, and productivity of five colonies at different latitudes in the 2021-2022 breeding season through the establishment of permanent plots. Direct comparisons between current and historical estimates were not possible due to differences in burrow assessment methods. As a remedy, I compared detection probabilities obtained by two different methods, burrowscoping and handgrubbing, and estimated a correction factor to allow for comparisons. Inter-rater reliability of the estimates was also evaluated. My findings show that estimates can be influenced by both data collection method and double-observer, even with experienced individuals. Nevertheless, every breeding parameter remained high in all colonies included in this study, suggesting an overall healthy breeding status in Newfoundland puffin populations, even in those where no historical data are available. This makes Newfoundland colonies the largest puffin aggregation worldwide with no signs of breeding failure in this declining species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".