Development of an Ecosystem Sensitivity Model Regarding Mercury Levels in Fish Using a Preference Modeling Methodology: Application to the Canadian Boreal System
Bibliographic record
Abstract
The objective of this study is to use a preference modeling methodology as a predictive tool to roughly assess the sensitivity of the ecosystems regarding mercury (Hg) concentrations in fish. We apply a preference modeling methodology to rank lakes within the boreal forest from highest to lowest Hg concentrations in fish using simple environmental factors. Among the numerous variables influencing Hg fate in the environment, we only retain simple key indicators that are expected to influence Hg concentrations in fish tissue such as watershed characteristics of the lake (percentage of the catchment area of the lake, ratio of drainage area versus lake area, percentage of the drainage area of the lake as wetlands, land use, and clear-cutting), lake characteristics (chlorophyll, dissolved organic carbon, pH, and fishing intensity), and atmospheric Hg inputs. Preliminary results of modeling that we carried out using a set of Canadian lakes of boreal forest data are promising. With only a minimum set of criteria, we are able to reproduce the trends of Hg contamination in fish caught in six regions of the Canadian boreal forest and classify the sensitivity of the ecosystems to Hg loadings in three categories: high, medium, and low.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".