Effects of Seabird Vectors on the Fate, Partitioning, and Signatures of Contaminants in a High Arctic Ecosystem
Bibliographic record
Abstract
Seabirds bioaccumulate contaminants from prey, transport them to their nesting sites, and deposit them in their excreta and carcasses, thereby focusing marine-derived contaminants into remote, terrestrial receptor sites. In the case of organochlorine chemicals transported by northern fulmars (Fulmarus glacialis) to a High Arctic seabird colony on Devon Island, Nunavut, Canada (76°13'N, 89°14'W), this contaminant pathway dominates all others. In freshwater ponds below the nesting cliffs, concentrations of organochlorine contaminants characteristic of fulmar input were 2- to 45-fold higher in sediments and water (depending on seabird input to the particular pond) than in ponds remote from the colony. Air-water fugacity quotients for the ponds decreased with seabird input, indicating that fulmar contaminant input shifts air-water partitioning to increasingly favor volatilization to air. Although contaminant evasion from water was favored, direct evidence of it was not detected in air samples. For PCBs, congener profiles of pond sediments or water became more similar to seabird sources as seabird input increased, and less similar to air profiles. Based on measurements of contaminants in fulmars and other local environmental media, this study presents the first application of fugacities and multivariate source apportionment statistics to resolve seabird biological vectors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".