Mercury in aquatic foodwebs: refining the use of mercury in energetics models of wild fish populations
Bibliographic record
Abstract
Contaminant accumulation models are increasingly used to estimate bioenergetics of fish populations. Often, required inputs for these models—namely growth and contaminant accumulation in fish and their diet—are estimated rather than measured directly. Using a methylmercury (MeHg) accumulation model, I demonstrated that certain methods for estimating these inputs can result in significant error in model estimates of consumption and activity. I identified two key components in minimizing this error: First, input estimates based on data collected late vs. mid-summer were less variable. Second, seasonal patterns in fish dietary McHg were identified; thus, seasonal estimates of dietary MeHg were biased compared to annual estimates, which translated into biases around estimates of consumption and activity. Also, I developed predictive models of annual MeHg in zoobenthos from lake pH and DOC. Similar published models for zooplankton suggest that environmental factors affecting McHg in littoral foodwebs are similar to those in pelagic foodwebs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".