Pup production of Harp Seals in the Northwest Atlantic in 2017 during a time of ecosystem change
Bibliographic record
Abstract
Photographic and visual aerial surveys were conducted off Newfoundland and Labrador (”the Front”), and in the Gulf of St. Lawrence (“Gulf”) in March 2017 to estimate pup production of Northwest Atlantic harp seals (Pagophilus groenlandicus). Traditionally, harp seals pup (whelp) in three general areas; the southern Gulf of St. Lawrence, the northern Gulf of St. Lawrence, and off the east coast of Newfoundland and Labrador. After extensive reconnaissance, four whelping areas were identified: one in each of the southern and northern Gulf, and two at the Front. We estimated a total pup production in 2017 of 746,500 (SE=89,900, CV=12%), the lowest since 1994. Most (96%) pups were born at the Front (714,600 pups, SE=89,700). Very few pups were born in the southern Gulf (18,300, SE=1,500) and no whelping concentrations were observed prior to March 5, approximately one week later than previously observed. This is far lower than the 2012 survey estimate of 115,500 (SE=15,100) for the same area. Pup production in the northern Gulf was also lower than in previous years, at 13,600 (SE=3,000). The timing of births in the southern Gulf was much later than normal in 2017, and unusually early pupping at the Front suggests that some females from the Gulf herd may have moved to the Front to whelp due to a lack of ice suitable for pupping (i.e., thin first year) in the Gulf. Harp seals whelp in large concentrations. While one large whelping concentration formed at the Front, approximately 15% of the pupping at the Front occurred in small, dispersed groups which formed later than observed in previous years. Given the unusual ice conditions, distribution of whelping seals, and timing of pupping, assessing the results of the 2017 surveys relative to other estimates of pup production in the Northwest Atlantic is challenging and indicates the ongoing difficulties of assessing a population that is being impacted by climate change.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".