Modeling mercury concentrations in northern pikes and walleyes from frequently fishes lakes of Abitibi-Témiscamingue (Québec, Canada): a GIS approach
Bibliographic record
Abstract
Using readily available geospatial data and statistical analyses we constructed models to predict mercury concentrations in northern pikes (Esox lucius) and walleyes (Stizostedion vitreum) in lakes frequently used by sport fishers, urban anglers and subsistance fishers in the Abitibi-Témiscamingue region (Canada). Mercury concentrations in northern pikes were predicted with 74% accuracy using three variables: lake order, fraction of the lake watershed with gentle to moderate slopes (steepness 2%–6%) and the fraction of the watershed with mature forest cover. To construct the walleye model, we divided lakes into 3 categories: (1) lakes with mines or mine tailings located less than 1 km away from the shore; (2) lakes located on the Lake Ojibway-Barlow clay plain; and (3) lakes outside the clay plain. For watersheds without mines, walleye Hg concentrations were predicted with over 77% accuracy using the fraction of wetlands in the watershed.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".