Reduction Of Mercury Concentration In Fish Through Intensive FishingOf Lakes: A Preliminary Testing Of Assumptions
Bibliographic record
Abstract
Intensive fishing of lakes has been indicated in the literature as a means of reducing mercury concentrations in fish. However, the controlling process by which this occurs remains unclear. Three assumptions are generally put forward to explain the reduction. The first is that intensive fishing would affect the total mercury balance of the lake and reduce the mercury bioaccumulation in fish. The second is that the fish diet would be affected, especially for piscivorous fish feeding on smaller prey fish with lower mercury concentration following intensive fishmg of larger prey fish. The third assumption is that the rate of growth of the fish remaining after intensive fishing would increase since competition for food would generally be reduced. Testing of these assumptions were made using fish data from three natural lakes located in the James Bay territory of northern Quebec, Canada, where intensive fishmg occurred in 1998. Dominant species of fish were considered and mass and mercury concentrations of individual fish were expressed as a function of fish age. A bioenergetics model was used to compute the rate of feeding of fish. A mercury bioaccumulation model was used to relate mercury concentration in fish to intake of Transactions on Ecology and the Environment vol 60, © 2003 WIT Press, www.witpress.com, ISSN 1743-3541
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".