Forty-Year Decline of Organic Contaminants in Eggs of Herring Gulls (<i>Larus argentatus</i>) from the Great Lakes, 1974 to 2013
Bibliographic record
Abstract
Following the discovery of widespread adverse reproductive effects in fish-eating colonial waterbirds nesting in the Canadian Great Lakes, Environment Canada started monitoring contaminants in Herring Gull (Larus argentatus) eggs in 1974. Current and historical concentrations and rates of decline of legacy contaminants (Polychlorinated Biphenyls [PCBs], 2,3,7,8-tetrachlorodibenzo-p-dioxin [TCDD] and organochlorine pesticides) in Herring Gull eggs from 15 Great Lakes colonies over 40 years are reported here. Large declines in contaminant concentrations were found in all colonies from the first year of reporting to 2013, with mean percent declines ranging from 72.7% for Σ chlordane to 95.2% for Mirex, indicating reduced availability of contaminants to wildlife. First-order exponential decay regressions indicated that rates of decline in eggs varied among compounds. Herring Gulls from Strachan Island (St. Lawrence River), for example, had the highest rates of decline for Dieldrin and Hexachlor Epoxide, whereas those from Middle Island (Lake Erie) had the lowest rates of decline for TCDD and PCBs, and those from Gull Island (Lake Michigan) had the lowest rates of decline for HCB and Mirex. Exponential rates of decline in Herring Gulls mirrored those previously reported for Great Lakes fish, thus demonstrating that Herring Gulls are a good fish-eating indicator species.
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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 | 0.000 |
| 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".