The Internal Distribution of Nickel and Thallium in Two Freshwater Invertebrates and its Relevance to Trophic Transfer
Bibliographic record
Abstract
Although nickel and thallium are present at potentially harmful concentrations in some lakes, there is little information on their bioaccumulation and transfer up aquatic food webs. To measure the propensity of animals for accumulating and transferring these contaminants along food chains, we exposed two common types of invertebrates, an insect (Chironomus riparius) and a worm (Tubifex tubifex), to these metals spiked into sediment. We then measured the subcellular distribution of Ni and Tl in these invertebrates to estimate the likelihood that these metals will have toxic effects on these prey or be transferred to higher trophic levels. In both species, at least half of their Ni and TI was present in fractions that are purportedly detoxified (granules and metal-binding proteins). Furthermore, based on information in the literature concerning prey subcellular fractions that are likely to be trophically available (TAM), we estimate that much of the Ni and TI in these animals (43-84%) is available for transfer to a predator. To test this prediction, we fed these invertebrates to the alderfly Sialis velata, and measured the efficiency with which this predator assimilated Ni and Tl from each prey type. The majority of both trace metals (58-83%) was assimilated by the predator, which suggests that these contaminants would be easily transferred along aquatic food chains and that models describing Ni and Tl accumulation by aquatic animals should consider food as a source of these metals. The proportion of metal that could potentially be taken up by a consumer (% TAM) and the actual percentage assimilated by S. velata fell on or reasonably close to a 1:1 line for the 4 prey-metal combinations.
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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".