Adjusting for Temporal Change in Trophic Position Results in Reduced Rates of Contaminant Decline
Bibliographic record
Abstract
The development of ecological tracers to track the flow of energy and nutrients through food webs has provided new insights into the factors that are important in regulating diet composition in wildlife. The Great Lakes Herring Gull Monitoring Program has provided information regarding temporal trends in levels of bioaccumulative contaminants since the early 1970s. In recent years, data from this program have also been generated to examine ecological changes in the Great Lakes. Because the contaminants that are evaluated as part of this program biomagnify, food is the primary determinant of contaminant concentrations in the eggs that are analyzed annually. Fluctuations in diet composition could affect the interpretation of temporal trends by affecting exposure to contaminants. Retrospective analyses involving ecological tracers, i.e., stable nitrogen isotopes and fatty acids, have shown temporal change in the diets of Great Lakes herring gulls at some monitoring colonies. These dietary differences have led to temporal variation in the trophic position of herring gulls. Given that higher trophic level organisms incur greater exposure to biomagnifying contaminants, it is necessary to adjust for these temporal changes in trophic position to get an accurate indication of how contaminant burdens are changing within the Great Lakes ecosystem. Here, we outline a method to adjust for temporal changes in indicator species trophic position and discuss how these adjustments affect the interpretation of contaminant temporal trend monitoring data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".