Toxicity and bioaccumulation of tributyltin in <i>Hyalella azteca</i> from freshwater harbour sediments in the Great Lakes Basin, Canada
Bibliographic record
Abstract
This study was designed to evaluate the risk that tributyltin (TBT) levels in harbour sediments pose to the freshwater invertebrate Hyalella azteca and to rank TBT as an environmental concern compared with inorganic metal species. Four-week toxicity tests and 2-week bioaccumulation tests were conducted with sediments collected from five harbours historically contaminated with TBT: Montreal, Kingston, Toronto, Hamilton, and Port Weller. While there was no observable effect of TBT on survival or growth of H. azteca, bioaccumulation of TBT exceeded levels expected to cause chronic toxicity in some sediments from Kingston, Montreal, and Port Weller. There was a positive relationship between TBT in H. azteca and TBT in sediments (r 2 = 0.72), although TBT in field sediments was less bioavailable than in laboratory-spiked sediments. Body concentrations of arsenic, cadmium, chromium, cobalt, lead, manganese, nickel, and thallium were well below levels expected to cause toxicity in H. azteca. However, overlying water concentrations of copper and zinc approached or exceeded levels of concern in Toronto, Hamilton, and Port Weller sediments. This research suggests that levels of TBT in harbour sediments may cause chronic toxicity to H. azteca and freshwater invertebrate species of similar sensitivity and that copper and zinc also pose a risk at these sites.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".