Effects of nutrient loading and planktivory on the accumulation of organochlorine pesticides in aquatic food chains
Bibliographic record
Abstract
The effects of nutrients and planktivory on the accumulation of hydrophobic organic contaminants (HOCs) in aquatic food chains were investigated in large lake enclosures. Food-chain compositions in the enclosures were manipulated by additions of planktivorous fish (+F), nutrients (+N), both nutrients and fish (+NF), or received no additions (-NF). The treatments resulted in higher plankton but lower zooplankton biomass in the +NF enclosures than in the other enclosures. Once enclosure communities were established, a suite of organochlorine pesticides (alpha-hexachlorocyclohexane, methoxychlor, heptachlor, cis- and trans-chlordane, cis- and trans-nonachlor, and mirex) was added to all enclosures in amounts sufficient to obtain initial concentrations in the epilimnion of approximately 15 ng/L. Dissipation of HOCs from the water and accumulation in phytoplankton, zooplankton, and fish were monitored for four months. The HOC concentrations in plankton did not differ significantly across treatments. However, on a total-mass basis, greater amounts of HOCs were sorbed to phytoplankton in the +NF enclosures (20%) than in the three other sets of enclosures. Concentrations in zooplankton of some HOCs differed significantly between treatments as a function of nutrient loading. Chlordane and nonachlor concentrations were greater in zooplankton from enclosures with no fish (+N, -NF) than in those from enclosures with fish (+F, +NF). The HOC residues in fish were highest in low-nutrient enclosures. The results demonstrate that fish predation and nutrient loading can modify the size-related processes of HOC partitioning and affect its accumulation in the aquatic food chain.
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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 | 0.001 |
| 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".