Accumulation, distribution, and toxicology of nickel in lake whitefish, Coregonus clupeaformis, and lake trout, Salvelinus namaycush, exposed through the dietary route of uptake
Bibliographic record
Abstract
Metal mining, milling, and smelting, metal processing, fuel combustion, and waste incineration activities release significant amounts of nickel (Ni) into freshwater systems. Benthic-feeding fish residing in Ni-contaminated systems are exposed to Ni through ingestion of contaminated food items and sediments. Laboratory-based research is needed to provide insight into the potential impacts of the chronic exposure of freshwater fish to dietary Ni. A short-term trial was conducted to investigate the uptake and toxicity of dietary Ni in adult lake whitefish ('Coregonus clupeaformis') and lake trout ('Salvelinus namaycush') fed diets containing 0, 1000, and 10000 [mu]g Ni/g, prepared with and without brine shrimp, over a period of 18 days. Results from this study were used to determine which diet concentrations, diet type, and fish species to use in the long-term experiment. In the long-term experiment, adult lake whitefish were fed diets containing 0, 10, 100, and, 1000 [mu]g Ni/g for 10, 31, and 104 days. The toxicity of Ni was assessed through the measurement of responses, through a range of levels of biological organization. (Abstract shortened by UMI.)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".