Wildlife as sentinels of human health effects in the Great Lakes--St. Lawrence basin.
Bibliographic record
Abstract
There is no existing formal, long-term program for gathering evidence of the incidence and severity of the health effects of toxic substances in wildlife. However, research-based studies of bald eagles, herring gulls, night herons, tree swallows, snapping turtles, mink, and beluga over the past 30 years have revealed a broad spectrum of health effects in the Great Lakes-St. Lawrence basin including thyroid and other endocrine disorders, metabolic diseases, altered immune function, reproductive impairment, developmental toxicity, genotoxicity, and cancer. These effects occurred most often and were most severe in the most contaminated sites (Green Bay, Saginaw Bay, Lake Ontario, the St. Lawrence estuary, and more recently, Lake Erie), some of which are International Joint Commission-designated Areas of Concern (AOCs). In all cases, a strong argument can be made for an environmental etiology, and in many cases for the involvement of persistent organic pollutants, particularly polychlorinated biphenyls, polychlorinated dibenzo-(italic)p(/italic)-dioxins, and polycyclic aromatic hydrocarbons. For some, the association with particular contaminants is consistent with controlled studies, and in some, dose-response relationships were documented. The biologic significance of these health impairments to the affected species is currently unclear, but they resemble those observed with increased incidence in human subpopulations in one or more AOCs. Formalizing health effects monitoring of sentinel wildlife species by the parties to the Canada-USA Great Lakes Water Quality Agreement is required. This would facilitate the optimal use of sentinel wildlife health data in a larger, epidemiologic weight-of-evidence context upon which to base decisions and policies regarding the effects of chemical exposures on human populations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".