Peer Review #2 of "Premature feather loss among common tern chicks in Ontario: the return of an enigmatic developmental anomaly (v0.1)"
Bibliographic record
Abstract
In July 2014, we observed premature feather loss (PFL) among non-sibling, common tern Sterna hirundo chicks between 2 and 4 weeks of age at Gull Island in northern Lake Ontario, Canada.Rarely observed in wild birds, to our knowledge PFL has not been recorded in terns since 1974, despite the subsequent banding of hundreds of thousands of tern chicks across North America alone.The prevalence, 5% of chicks (9/167), and extent of feather loss we report is more extreme than in previous reports for common terns but was not accompanied by other aberrant developmental or physical deformities.Complete feather loss from all body areas (wing, tail, head and body) occurred over a period of a few days but all affected chicks appeared vigorous and quickly began to grow replacement feathers.All but one chick (recovered dead and submitted for post-mortem) most likely fledged 10-20 days after normal fledging age.We found no evidence of feather dystrophy, concurrent developmental abnormalities or nutritional shortfall among affected chicks.Thus, the PFL we observed among common terns in 2014 was largely of unknown origin.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.One hypothesis is that PFL may have been caused by unidentified pathogens or toxins welling up from these deep waters along the shoreline but current data are insufficient to test this.PFL was not observed among common terns at Gull Island in 2015, although we did observe similar feather loss in a herring gull Larus argentatus chick in that year.Comparison with sporadic records of PFL in other seabirds suggests that PFL may be a rare, but non-specific response to a range of potential stressors.PFL is now known for gulls, penguins and terns.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: yes | Not applicable | medium |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: yes | Not applicable | medium |
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.075 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".