PREDICTING MERCURY CONCENTRATION IN FISH USING MASS BALANCE MODELS
Bibliographic record
Abstract
Mass balance models have frequently been used with laboratory-derived bioenergetic models to examine the accumulation of mercury (Hg) in fish. The accumulation of Hg in fish has usually been successfully described by these models. However, this has generally been achieved by adjusting the parameters of these models until there was a close fit between observed and predicted values. In this study, we present a simple Hg mass balance model (MMBM) to predict Hg concentration in fish. This MMBM was applied with three methods of estimating food consumption rates to predict Hg concentration in three freshwater fish species. The MMBM accurately predicted the accumulation of Hg in the three fish species examined in this study when it was combined with food consumption rates that were determined with a radioisotopic method. The MMBM tended to underestimate Hg concentration in fish when it was combined with food consumption rates determined using laboratory-derived bioenergetic models, possibly because activity costs derived under laboratory conditions do not adequately represent activity costs of fish in the field. When feeding rates were estimated with a bioenergetic model implemented with site-specific estimates of activity costs, the MMBM accurately predicted the concentration of Hg in fish. Therefore, until activity costs can be accurately estimated in situ, predictions obtained with the MMBM implemented with a laboratory-derived bioenergetic model should be interpreted cautiously.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".