Food Web Magnification of Persistent Organic Pollutants in Poikilotherms and Homeotherms from the Barents Sea
Bibliographic record
Abstract
Food web magnification of persistent organic pollutants (POPs) was determined for the Barents Sea food web using 615N as a continuous variable for assessing trophic levels (TL). The food web investigated comprised zooplankton, ice fauna and fish (poikilotherms, TL 1.7-3.3), and seabirds and seals (homeotherms, TL 3.3-4.2), with zooplankton representing the lowest and glaucous gull the highest trophic level. Concentrations of lipophilic and persistent organochlorines were orders of magnitude higher in homeotherms than in poikilotherms. These compounds had significantly higher rates of increase per trophic level in homeotherms relative to poikilotherms, with the highest food web magnification factors (FWMFs) for cischlordane and p,p'-DDE. Some compounds, such as transnonachlor and HCB, had similar rates of increase throughout the food web, whereas compounds that are more readily eliminated (gamma-HCH) showed no relationship with trophic level. It is preferable to calculate FWMFs with regard to thermal groups, because the different energy requirements and biotransformation abilities between poikilotherms and homeotherms may give different rates of contaminant increase with trophic level. When biomagnification is compared between ecosystems, FWMFs are preferable to single predator-prey biomagnification factors. FWMFs represent a trophic level increase of contaminants that is average for the food chain rather than an increase for a specific predator-prey relationship. The Barents Sea FWMFs were generally comparable to those determined for marine food webs with similar food chain lengths in the Canadian Arctic.
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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".