The long-term effects of water resource development on blood mercury levels and the trophic ecology of Namew (lake sturgeon, <i>Acipenser fulvescens</i>)
Bibliographic record
Abstract
Water resource development can alter the movement and ecology of sturgeons. We studied total (THg) and methylmercury concentrations in whole blood sampled non-lethally from namew (Moose Cree L-dialect, lake sturgeon, Acipenser fulvescens), an endangered and culturally important subsistence fish. Namew were sampled from two tributaries within the Moose Cree Homeland: the Lower Mattagami River (an impacted system with four hydroelectric generating stations) and the North French River (a reference system that is free-flowing system with no development). Results indicated namew from the North French River had higher blood [THg] than those from the Mattagami River. Further modeling showed that trophic position was the primary driver of these differences, with North French namew having the highest nitrogen isotope ratios. Based on further isotope modeling, crayfish were major components of namew diet at all sites, while other prey items differed between sites. Specifically, namew with unobstructed access to the lower watershed had notably more enriched isotope values when compared to the freshwater benthic macroinvertebrates sampled, implying that other prey not captured herein may contribute to their diets and [THg]. Overall, we found differences in namew’s trophic ecology but no elevation in blood mercury levels at a site impacted by hydroelectric operations 60+ years post-impoundment.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".