Lake Erie fish safe to eat yet afflicted by algal hepatotoxins
Bibliographic record
Abstract
Abstract Microcystin toxins from harmful algal blooms (HABs) can accumulate and persist in fish, raising dual concerns about human health risks from consumption and the potential for detrimental impacts on fish populations. However, there are fundamental unknowns about the relationship between HABs and fish populations driven by a lack of field information on toxin accumulation and retention over space and time. We conducted a field study to assess human health risks from consuming fish caught across all life stages of a HAB and to determine the pervasiveness of potentially harmful levels of microcystins on fish populations. We collected 190 fish in 2015 and 2017 from Lake Erie, a large freshwater ecosystem that is highly productive for fisheries and is an epicenter of HABs and microcystin toxicity events. Muscles and livers were analyzed for total microcystins, which was used to conduct a human health risk assessment for comparison against fish consumption advisory benchmarks available for Lake Erie. We find low human health risk from muscle consumption following the World Health Organization’s safety thresholds. However, all fish across capture dates had microcystins in their livers at levels shown to cause adverse effects, suggesting a pervasive and underappreciated toxic stressor. These data demonstrate that microcystins are retained in fish livers well beyond the cessation of HABs and calls for additional research to better understand the effects of sublethal toxic exposures for fish population dynamics, conservation, and related ecosystem services.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".