Studying Chinook salmon in northern river ecosystems through ecological methods and Indigenous, Teslin Tlingit knowledge
Bibliographic record
Abstract
In this study, we examined Pacific salmon decline and ecosystem function through Western science and Indigenous, Teslin Tlingit knowledge. We tested relationships of riparian tree growth and nitrogen composition at the limit of Pacific salmon distribution on the Teslin Tlingit Council (TTC) Traditional Territory in Southern Yukon, studied ecosystem roles of salmon and population declines in the area, and the interactions of these processes. Within sites, tree growth was positively related to salmon escapement at all salmon-bearing sites and not at the negative (salmon-free) control site. Mean δ15N was significantly higher at salmon-bearing sites compared to the negative control, showing similar patterns to comparable studies. Between sites, mean site tree growth did not show a clear response to salmon. Interviews conducted with Teslin Tlingit knowledge holders revealed measures of a healthy salmon run and large population declines that have negatively impacted local ecosystems (particularly bears, key to delivering salmon nutrients to terrestrial ecosystems) and human wellbeing on the Traditional Territory. Although this study was limited by available sites and data, we demonstrate that the health of salmon and riparian forests on the Traditional Territory are deeply linked, and the importance of considering multiple ways of knowing to improve ecological research.
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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 | 0.001 | 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 | 0.001 |
| 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".