Migratory pathways and winter destinations of Northern Gannets breeding at Helgoland (North Sea): known patterns and increasing importance of the Baltic Sea
Bibliographic record
Abstract
Abstract We analysed the migratory behaviour of adult Northern Gannets (Morus bassanus) breeding at Helgoland in the North Sea, based on data obtained from geolocation devices in the non-breeding season 2016–2017. Birds moved east and south-west to a broad range of wintering sites, ranging from the western Baltic Sea to North-West Africa. Three out of 12 birds spent the winter in Africa, while 9 birds wintered in Europe, with the primary wintering sites in the North Sea. All but one tagged bird spent some time in the Baltic Sea or in the transitional waters between the North Sea and Baltic Sea. We also analysed data from online databases (dofbasen.dk, ornitho.de) and the German Seabirds at Sea database to explore the extent to which Northern Gannets used the western Baltic Sea, as well as the Kattegat and Skagerrak, during the winter months. Records of Northern Gannets in Danish waters have increased substantially over the last 18 winters, with particular increases in the Baltic Sea. There was also a notable increase in sightings of Northern Gannets in German Baltic Sea waters, but this occurred later than in the more northerly Danish waters. Both analyses demonstrated that Northern Gannets explored the western part of the Baltic Sea, as well as the Kattegat and Skagerrak, increasingly intensively. This recent increase in sightings is in accord with the establishment and exponential increase in the nearest breeding colony of Northern Gannets at Helgoland.
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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 | 0.000 |
| 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 | 0.000 |
| 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".