The role of cannibalism and contaminant source on bioaccumulation in aquatic food webs
Bibliographic record
Abstract
Two aspects of bioaccumulation in an aquatic food web are explored. First, the possible implications of cannibalism, including the scavenging of conspecifics, as a factor influencing food web bioaccumulation and biomagnification are explored by examining the behavior of total polychlorinated biphenyls (PCBs) in a simple aquatic food web consisting of plankton, juvenile and adult Mysis relicta, Diporeia, and alewife. From an analysis of trophic transfer efficiencies and food consumption rates, it is concluded that, for M. relicta, a maximum extent of cannibalism in a population is about 10%, although certain individuals may be more cannibalistic. The model suggests that cannibalism and scavenging of dead conspecifics generally result in an increase in concentration by self-biomagnification, but the effect is small and unlikely to exceed 5% on the average. Concentration differences also are likely to result from changes in the relative amounts of the dietary components. Highly cannibalistic individuals may achieve higher levels of bioaccumulation. In extreme cases, the food web model becomes mathematically unstable because of excessive feedback of high concentrations. A major implication is that differences in extent of cannibalism and scavenging probably contribute significantly to natural concentration variation in a population. Second, and more important, is the effect of benthic versus pelagic sources, especially when significant fugacity differences exist between these zones. A simple method is described by which the separate contributions from these sources can be estimated for organisms at higher trophic levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".