Extirpation of Lake Sturgeon in an Ontario Lake Following Dam Construction and Watershed Diversion Confirmed by Indigenous Traditional Knowledge and Sedimentary <scp>eDNA</scp>
Bibliographic record
Abstract
Damming and water diversions for hydroelectricity, flood control, irrigation, and consumption have had profoundly negative consequences for wildlife, ecosystems, and local peoples globally. The assessment and monitoring of ecological impacts have been common practice for only the last half century and are vital in testing population trends as habitats become increasingly fragmented and degraded. Many systems, including the focus of our study, the Upper Kenogami Watershed (UKW), have been subject to large-scale damming and diversions prior to modern environmental assessments, leaving the consequences largely unknown. Local Anishinaabe communities, Long Lake #58 and Ginoogaming, have long emphasized the many negative consequences for the environment and non-human kin caused by the Upper Kenogami diversion, including the local extirpation of lake sturgeon from Long Lake. Here we find that the extirpation of lake sturgeon from Long Lake coincided with the construction of the Kenogami control dam, evidenced through Indigenous Traditional Knowledge (ITK) within Ginoogaming and Long Lake #58 First Nations, as well as sedimentary eDNA signatures. We thus show how both ITK and molecular insights together reveal a more compelling understanding of the impacts of freshwater entrainment and damming on the UKW lake sturgeon population. We use this information to suggest what is needed to rebuild sustainable populations and relationships.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".