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Record W2016713987 · doi:10.5253/078.095.0115

Incidence of Artifact Ingestion in Mute Swans and Tundra Swans on the Lower Great Lakes, Canada

2007· article· en· W2016713987 on OpenAlexafffundabout
Jenna E. Bowen, Scott A. Petrie

Bibliographic record

VenueArdea · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsBirds CanadaWestern University
FundersBird Studies Canada
KeywordsTundraWaterfowlBlack swan theoryIngestionShot (pellet)EcologyFisheryBiologyArcticHabitat

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.195
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2007
Admission routes3
Has abstractyes

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