Biological and chemical factors of importance in the bioaccumulation and trophic transfer of persistent organochlorine contaminants in arctic marine food webs
Bibliographic record
Abstract
Recent studies of arctic marine food webs have provided detailed insights regarding the biological and chemical factors that influence the bioaccumulation and trophic transfer of persistent organochlorine (OC) contaminants in aquatic systems. The present paper summarizes the recent literature with an emphasis on identifying important ecological factors for explaining variability of OC concentrations among organisms. The Arctic ecosystem has a number of unique attributes, including long food chains, reduced diversity of species, similar food webs across the entire region, and limited influence from pollution point sources. Lipid content, body size, age, gender, reproduction, habitat use, migration, biotransformation, seasonal changes in habitat conditions, feeding ecology, and trophic position have all been demonstrated to influence OC concentrations and bioaccumulation in arctic marine biota. The relative importance of each factor varies among OCs and organisms. Diet or trophic level is the dominant factor influencing OC concentrations and dynamics in seabirds and marine mammals, although biotransformation can significantly influence nonrecalcitrant OCs, such as hexachlorocyclohexane isomers. Dietary accumulation of OCs is also an important route of exposure for arctic fish and zooplankton, and biomagnification of OCs may also occur among these organisms. To date, only limited attempts have been made to model trophic transfer of OCs in the arctic marine food web. Although models developed to assess OC dynamics in aquatic food webs have included some biological variables (e.g., lipid content, feeding rate, diet composition, and growth rate), selection of processes included in these models as well as their mathematical solutions and parameterization all introduce simplification. This reduces biological validity of the models and may be particularly problematic in a highly seasonal environment, such as the Arctic Ocean.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".