Incidence of Artifact Ingestion in Mute Swans and Tundra Swans on the Lower Great Lakes, Canada
Bibliographic record
Abstract
Although lead poisoning is common in swans, no information exists on the prevalence of lead artifact ingestion in swans using the lower Great lakes (LGL). We examined artifact ingestion (lead and non-toxic) in Mute Swans Cygnus olor and Tundra Swans Cygnus columbianus collected on the LGL in Ontario (1999–2003) following the 1999 ban on use of lead shot for waterfowl hunting in Canada. A larger proportion of Mute Swans (19.8% of 243 birds) contained artifacts than did Tundra Swans (6.5% of 77 birds), possibly due to the fact that Mute Swans feed exclusively in aquatic habitats. Overall, 14% of Mute Swans contained nontoxic shot, 6% contained lead shot and 1.6% contained fishing tackle; 4% of Tundra Swans contained non-toxic shot and 2.6% contained lead shot. Adult Mute Swans (22.7%) had a higher incidence of artifact ingestion than did cygnets (8.9%), but there were no age-related differences in Tundra Swans. No sex-related differences in artifact ingestion were detected in either species. Given the overall frequency of shot ingestion in Mute Swans (20% of birds), lead toxicosis probably was a significant mortality factor for this species on the LGL before the lead shot ban. As only 1.6% of Mute Swans and no Tundra Swans contained any form of fishing tackle, angling related injuries and mortalities are likely lower in the LGL than has been reported for swans in Europe. Presently, lead toxicosis is likely having a low to moderate effect on Mute Swans and a minimal effect on Tundra Swans on the LGL.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".