Influence of Prey Type on Nickel and Thallium Assimilation, Subcellular Distribution and Effects in Juvenile Fathead Minnows (<i>Pimephales promelas</i>)
Bibliographic record
Abstract
Because fish take up metals from prey, it is important to measure factors controlling metal transfer between these trophic levels so as to explain metal bioaccumulation and effects in fish. To achieve this, we exposed two types of invertebrates, an oligochaete (Tubifex tubifex) and a crustacean (Daphnia magna), to environmentally relevant concentrations of two important contaminants, nickel (Ni) and thallium (Tl), and fed these prey to juvenile fathead minnows (Pimephales promelas). We then measured the assimilation efficiency (AE), subcellular distribution and effects of these metals in fish. Fish assimilated dietary Tl more efficiently from D. magna than from T. tubifex, and more efficiently than Ni, regardless of prey type. However, the proportion of metal bound to prey subcellular fractions that are likely to be trophically available (TAM) had no significant influence on the efficiency with which fish assimilated Ni or Tl. In fish, the majority of their Ni and Tl was bound to subcellular fractions that are purportedly detoxified, and prey type had a significant influence on the proportion of detoxified Ni and Tl in fish. We measured higher activities of cytochrome C oxidase and glutathione S-transferase in fish fed D. magna compared to fish fed T. tubifex, regardless of the presence or absence of Ni or Tl in prey. However, we measured decreased activities of glutathione S-transferase and nucleoside diphosphate kinase in fish fed Tl-contaminated D. magna compared to fish from the three other treatment 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".