J. Great Lakes Res. 29 (Supplement 2):267–282 Internat. Assoc. Great Lakes Res., 2003 Distribution and Food-web Transfer of Mercury in
Bibliographic record
Abstract
ABSTRACT. Mercury (Hg) concentrations were measured for the food webs and water of Napoleon Gulf (Uganda) and Winam Gulf (Kenya) in northern Lake Victoria. Water total mercury (THg) concen-trations in Lake Victoria range from 1.7 to 5.8 ng/L, while methylmercury (MeHg) concentrations range from 0.2 to 1 ng/L. Water Hg concentrations in Lake Victoria are higher than in temperate great lakes, including Lakes Baikal, Michigan, and Ontario, but the top predator Nile perch have relatively low THg concentrations compared to temperate piscivorous fish. While the water Hg concentrations are similar between Napoleon and Winam gulfs, the THg concentrations in biota are significantly higher in Napoleon Gulf than in the same species from Winam Gulf, which may be due to biogeochemical differ-ences in each gulf. THg concentrations in Nile perch and Nile tilapia consistently increase with total length in both gulfs and the rates of increase are similar. The rates of THg bioaccumulation, as indicated by the regression slopes of log-THg vs. stable nitrogen isotope values for each food web (slopes of 0.163 and 0.165 for Napoleon and Winam gulfs, respectively), are within the ranges of bioaccumulation rates observed in temperate and tropical lakes elsewhere which suggests that Hg bioaccumulates at a similar rate in diverse aquatic food webs, regardless of latitude or species composition. INDEX WORDS: Mercury, food webs, stable nitrogen isotopes, water, Lake Victoria.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.119 | 0.063 |
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".