Tracking Aquatic Biodiversity With Environmental <scp>DNA</scp> : A Study in Quebec's Mining Region
Bibliographic record
Abstract
ABSTRACT In Quebec's James Bay region, mining activities pose significant risks to local aquatic biodiversity due to habitat disturbance and potential contaminant release. This study evaluates the efficacy of environmental DNA (eDNA) for detecting and monitoring fish species in areas affected by mining operations, specifically at an active gold mine and a prospective lithium mine. Over two sampling campaigns, eDNA analyses identified the presence of four target fish species, including species of ecological and cultural significance to the Cree communities. The eDNA method proved to be a sensitive and non‐invasive tool, capable of detecting species across large aquatic ecosystems and providing insight into species distribution and abundance in relation to environmental changes. Results indicate that certain species, like lake sturgeon or sauger, may be absent or less detectable in mining‐impacted areas, potentially due to habitat fragmentation and altered water quality parameters, including low pH and elevated heavy metal concentrations. Our findings support the integration of eDNA as a valuable monitoring tool for assessing biodiversity and establishing species presence baselines in sensitive ecosystems and highlight its potential for community‐led environmental management initiatives in Indigenous territories.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".