Metal Levels in Eggs of Waterbirds in the New York Harbor (USA): Trophic Relationships and Possible Risk to Human Consumers
Bibliographic record
Abstract
Health professionals are interested in evaluating the risks that heavy metals pose to eco-receptors and humans. The objective of this study was to examine levels of mercury (Hg), lead (Pb), cadmium (Cd), and other contaminants in waterbirds nesting in the New York harbor in 2012 to determine (1) whether there were species and locational differences, and (2) whether consumption of eggs posed a health risk to predators or humans. For arsenic (As), Pb, Hg, and selenium (Se), species contributed more to variations in levels than location; for Cd and chromium (Cr), location was more significant. Mean metal levels differed among species for all metals, except Cd. Highest levels were As (great black-backed gulls, Larus marinus), Cr (great egret, Ardea alba), Pb (Canada goose, Branta canadensis), and Hg and Se (black-crowned night heron, Nycticorax nycticorax). There were significant locational differences only for herring gulls (Larus argentatus); significant differences were found for all metals. Levels of Hg and Pb may be sufficiently high in eggs of some species to produce adverse effects in predators that eat them. The proportion of samples above 0.3 ppm Hg (U.S. Environmental Protection Agency [EPA] freshwater criteria for freshwater fish), the contaminant of health concern, ranged from 0% (Canada goose, great egret), to 14 and 27% in gulls, to 50% (black-crowned night heron). Some herring gull, great black-backed gull, and black-crowned night heron eggs had 0.5 ppm or higher Hg. Thus, human consumption of eggs may pose a risk to fetuses and young children.
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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".