Investigating the potential impacts of water quality on kokanee salmon in the Mätʼàtäna Män (Kathleen Lake) Watershed, Kluane National Park and Reserve, Yukon
Bibliographic record
Abstract
The Kathleen Lake Watershed in Kluane National Park and Reserve is home to a land-locked population of kokanee salmon that experienced a dramatic decline in the number of spawning individuals starting in 2001. As of 2018, evidence supporting possible reasons for this population crash remained elusive. In 2018, a water quality investigation was initiated in the Kathleen Lake Watershed to see if water quality impairments might be impacting the health and spawning of the kokanee. Extensive monitoring by Parks Canada and Environment and Climate Change Canada from 2020 to 2022 documented turbidity events and high concentrations of certain metals, principally selenium and aluminum, in Johobo Creek, a tributary stream that discharges to Sockeye Creek just downstream of the kokanee salmon spawning area. As salmon essentially navigate by smell, they may avoid or delay migration to the spawning areas in the basin headwaters due to the turbidity and/or metal concentrations. Minerals, metals, and very fine sediments appear to be released and transported because of ground ice thaw associated with warm weather events in the basin. Although the metals are from a natural, geological source in the Johobo Creek headwaters, it appears that climate change may be impacting the magnitude and/or timing of the release of these metals and associated fine sediments to the kokanee habitat. To better assess the impacts of the metals and turbidity events on the kokanee spawning behaviour and success, future research should include assessing kokanee migration behaviour in response to turbidity events.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".