Identifying the ecological drivers of total mercury concentrations in Brook Charr (Salvelinus fontinalis) populations across Western Newfoundland, Canada
Bibliographic record
Abstract
The transformation of mercury to the toxic methylmercury in anoxic lake sediment, along with its bioaccumulation and biomagnification in lacustrine food webs, makes it a potent environmental toxin with implications for both ecosystem and human health. Multiple ecological factors operating at different scales contribute to the movement of mercury into freshwater systems and its subsequent methylation and concentration in aquatic organisms. This thesis aims to identify ecological factors driving the bioaccumulation and biomagnification of mercury in lacustrine populations of brook charr (Salevelinus fontinalis) from western Newfoundland, Canada. The total mercury (THg) concentrations were measured in brook charr from 34 headwater lakes. The study examined several variables, including individual morphology, stable isotope compositions, and lake productivity, alongside topographical and land cover data derived from geographical information systems. A structural equation model (SEM) was developed to discern the direct and indirect effects of fish growth and size, chlorophyll-a concentration, catchment topography and land cover on mercury accumulation in brook charr. The analysis revealed a strong influence of catchment-scale factors on mercury bioaccumulation in brook charr. These findings highlight the need for employing optimal catchment management practices alongside continuous monitoring of water quality and ecosystem health in remote freshwater sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".