Time to refine mercury mass balance models for fish
Bibliographic record
Abstract
Mercury mass balance models (MMBMs) for fish are powerful tools for understanding factors affecting growth and food consumption by free-ranging fish in rivers, lakes, and oceans. Moreover, MMBMs can be used to predict the consequences of global mercury reductions, overfishing, and climate change on mercury (Hg) concentration in commercially and recreationally valuable species of fish. Such predictions are useful in decision-making by resource managers and public health policy makers, because mercury is a neurotoxin and the primary route of exposure of mercury to humans is via consumption of fish. Recent evidence has emerged to indicate that the current-day version of MMBMs overestimates the rate at which fish eliminate mercury from their bodies. Consequently, MMBMs overestimate food consumption by fish and underestimate Hg concentration in fish. In this perspective, we explore underlying reasons for this overestimation of Hg-elimination rate, as well as consequences and implications of this overestimation. We highlight emerging studies that distinguish species and sex as contributing factors, in addition to body weight and water temperature, that can play an important role in how quickly Hg is eliminated from fish. Future research directions for refining MMBMs are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".