Does contemporary premature feather loss among common tern chicks in Lake Ontario reflect persistent pollutants, enigmatic diseases or novel pathogens?
Bibliographic record
Abstract
We observed premature feather loss (PFL) among common terns Sterna hirundo at a small colony in northern Lake Ontario, Canada in July 2014. This condition is characterized by affected chicks losing all their wing, tail, head and body feathers several weeks after hatching. Rarely observed in wild birds, to our knowledge PFL in terns has not been recorded since 1974 (despite the banding of tens of thousands of tern chicks across North America since then). In July 2014, we observed PFL in chicks at between 2 and 4 weeks of age. The extent of feather loss was more extreme than in previous reports but was not accompanied by other aberrant developmental or physical deformities. Complete feather loss occurred over a period of a few days but all affected chicks quickly began to grow replacement feathers and all but one most likely fledged 10-20 days after normal fledging age. Feather samples, both shed feathers and re-growing live feathers, were collected from both affected chicks and normal individuals. One subsequently dead PFL chick was collected. Samples are awaiting further analysis. There was striking temporal association between the onset of PFL and persistent strong southwesterly winds that caused extensive mixing of near-shore, surface water with cool, deep lake waters. To our current knowledge it seems most probable that the PFL we observed in 2014 was caused by pathogens (viruses, bacteria, algal toxins) welling up from these deep waters along the shoreline but a direct link has not yet been made. The re-emergence of PFL in common terns may indicate acute health risks for birds and other wildlife in the Lake Ontario region and may also have potential for human health risks.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".