Productivity, embryo and eggshell characteristics, and contaminants in bald eagles from the Great Lakes, USA, 1986 to 2000
Bibliographic record
Abstract
Chlorinated hydrocarbon concentrations in eggs of fish-eating birds from contaminated environments such as the Great Lakes of North America tend to be highly intercorrelated, making it difficult to elucidate mechanisms causing reproductive impairment, and to ascribe cause to specific chemicals. An information- theoretic approach was used on data from 197 salvaged bald eagle (Haliaeetus leucocephalus) eggs (159 clutches) that failed to hatch in Michigan and Ohio, USA (1986-2000). Contaminant levels declined over time while eggshell thickness increased, and by 2000 was at pre-1946 levels. The number of occupied territories and productivity increased during 1981 to 2004. For both the entire dataset and a subset of nests along the Great Lakes shoreline, polychlorinated biphenyls (SigmaPCBs, fresh wet wt) were generally included in the most parsimonious models (lowest-Akaike's information criterion [AICs]) describing productivity, with significant declines in productivity observed above 26 microg/g SigmaPCBs (fresh wet wt). Of 73 eggs with a visible embryo, eight (11%) were abnormal, including three with skewed bills, but they were not associated with known teratogens, including SigmaPCBs. Eggs with visible embryos had greater concentrations of all measured contaminants than eggs without visible embryos; the most parsimonious models describing the presence of visible embryos incorporated dieldrin equivalents and dichlorodiphenyldichloroethylene (DDE). There were significant negative correlations between eggshell thickness and all contaminants, with SigmaPCBs included in the most parsimonious models. There were, however, no relationships between productivity and eggshell thickness or Ratcliffe's index. The SigmaPCBs and DDE were negatively associated with nest success of bald eagles in the Great Lakes watersheds, but the mechanism does not appear to be via shell quality effects, at least at current contaminant levels, while it is not clear what other mechanisms were involved.
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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 | 0.001 | 0.001 |
| 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".