Estimating food consumption rates of fish using a mercury mass balance model
Bibliographic record
Abstract
We present a simple method for estimating food consumption rates of fish in the field based on a mercury (Hg) mass balance model. This method requires the determination of fish age, size, and growth and Hg concentration in fish and their food. The model was validated using data obtained from a previously published laboratory experiment. A field test of the model showed that food consumption rates determined with the Hg mass balance model differed from independent estimates obtained with the 137 Cs method by only 0.6-16.1%. The model was applied to fish from various lakes in Quebec and Ontario. Food consumption rates estimated with the Hg mass balance model varied significantly both among species and among populations. Furthermore, female fish tended to eat 30-40% more food than males, probably to meet the larger energy requirement associated with egg production. A sensitivity analysis indicated that the Hg mass balance model was mostly responsive to variables that can be easily measured in the field, such as fish size and Hg concentration in fish and their food. By providing a low-effort approach to quantifying food consumption rates of fish in the field, this method may help to refine our understanding of the environmental factors that influence the quantity of food consumed by fish.
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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) | 0.001 | 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 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".