Evaluation of the human health threat associated with the hepatotoxin microcystin in the muscle and liver tissues of yellow perch (Perca flavescens)
Bibliographic record
Abstract
During the summer of 2006, the western basin of Lake Erie experienced a bloom of the toxigenic cyanobacterium Microcystis aeruginosa . Across 11 sites, intracellular, particulate-bound microcystin levels in the seston increased to levels that exceeded World Health Organization guidelines for drinking water exposure (1 µg toxin·L–1). In contrast, toxin concentrations in yellow perch ( Perca flavescens ) muscle tissue (n = 68) declined from June to August, were negatively related to algal toxin levels, and never exceeded a conservative chronic exposure concentration estimated using proposed United States Environmental Protection Agency (US EPA) guidelines. Microcystin concentrations in yellow perch liver exceeded US EPA chronic exposure guidelines, were on average 125 times higher than muscle toxin concentrations per unit dry weight, and varied little throughout the summer. With current guidelines, humans do not appear to be at risk when consuming the muscle tissue of Lake Erie yellow perch collected during large-scale cyanobacterial blooms. However, this study highlights the need for a better understanding of the trophic transfer of cyanobacterial toxins through aquatic food webs in diverse ecosystems with an emphasis on understanding if these compounds could accumulate sufficiently to affect human health.
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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) | 0.000 | 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".