Osprey: Worldwide Sentinel Species for Assessing and Monitoring Environmental Contamination in Rivers, Lakes, Reservoirs, and Estuaries
Bibliographic record
Abstract
In the United States, many fish and wildlife species have been used nationwide to monitor environmental contaminant exposure and effects, including carcasses of the bald eagle (Haliaeetus leucocephalus), the only top avian predator regularly used in the past. Unfortunately, bald eagles are sensitive to investigator intrusion at the nest. Thus, the osprey (Pandion haliaetus) is evaluated as a potential sentinel species for aquatic ecosystems. Several characteristics support the choice of the osprey as a sentinel species, including: (1) fish-eating diet atop the aquatic food web, (2) long-lived with strong nest fidelity, (3) adapts to human landscapes (potentially the most contaminated), (4) tolerates short-term nest disturbance, (5) nests spatially distributed at regular intervals, (6) highly visible nests easily located for study, (7) ability to accumulate most, if not all, lipophilic contaminants, (8) known sensitivity to many contaminants, and (9) nearly a worldwide distribution. These osprey traits have been instrumental in successfully using the species to understand population distribution, abundance, and changes over time; the effects of various contaminants on reproductive success; how contaminants in prey (fish on biomass basis) contribute to egg concentrations (i.e., biomagnification factors); and spatial residue patterns. Data summarized include nesting population surveys, detailed nesting studies, and chemical analyses of osprey egg, organ, blood, and feather samples for contaminants that bioaccumulate and/or biomagnify in aquatic food webs; and biochemical evaluations of blood and various organs. Studies in the United States, Canada, Mexico, Europe, and elsewhere have shown the osprey to be a useful sentinel species for monitoring selected environmental contaminants, including some emerging contaminants in lakes, reservoirs, rivers, and estuaries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".