The physical and chemical limnology of Yukon’s largest lake, Lhù’ààn Mân’ (Kluane Lake), prior to the 2016 ‘A’ąy Chù’ diversion
Bibliographic record
Abstract
Despite increasing evidence that large northern lakes are rapidly changing due to climate change, descriptive baseline studies of their physicochemical properties are largely lacking, limiting our ability to detect or predict change. This study represents a comprehensive scientific assessment of the limnology of Yukon’s largest lake: Lhù’ààn Mân’ (Kluane Lake), an important waterbody for local and First Nation communities, and key habitat for trout and salmon. Water sample and instrument data generated throughout 2015 describe distinct regions within the lake and their respective seasonal variability. A deep, glacially-influenced southern basin was characterized by cold, turbid, poorly stratified, unproductive, and nutrient-poor conditions; a shallower northwestern region (Tthe Kaala Daagur (Brooks/Little Arm)) was warmer, fully mixed, and more productive; a northeast region (’Ùha K’ènji (Talbot/Big Arm)) was clear and stratified with intermediate depth, temperature, productivity, and nutrient concentrations; and a central region had intermediate physicochemical conditions relative to the other three. This variability demonstrates the need for adequate spatial (within lake) and temporal (between seasons) monitoring of large northern lakes. In 2016, glacier recession within the watershed resulted in diversion of the lake’s primary inflow (‘A’ą̈y Chù’ (Slims River)). Our results, when used together with Indigenous knowledge, form a historical reference that enables assessments of the potential ecological consequences to Lhù’ààn Mân’.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".