A climatological approach to predicting water level of Great Slave Lake
Bibliographic record
Abstract
The subarctic northern Canada, including Great Slave Lake (GSL) in the Northwest Territories, is undergoing unprecedented warming – up to four times faster than the global average over the past 40 years. This warming has significant hydrological impacts, altering precipitation, evapotranspiration, and water balance of lakes and rivers. GSL, a crucial component of the Mackenzie River basin which has been gauged since 1934, has experienced extreme water level fluctuations in recent years, peaking with record high levels in September 2020 and having record lows in December 2023. These fluctuations directly impact water levels on the Mackenzie River, which has resulted in extreme flooding during high water levels and threatens navigability during low water levels, which is crucial for resupply of goods and services to isolated communities. In response, the Government of Northwest Territories (GNWT), Environment and Climate Change Canada (ECCC), and the University of Calgary have developed a hydraulic model to predict water levels and inform transportation planning. The GSL model uses historical hydrometric data and climatological information to estimate the residual net total supply (NTS) to the lake and incorporates stage-discharge relationships and level pool routing. Water level predictions are made up to 214 days ahead, potentially aiding in transportation planning downstream on the Mackenzie River. Model concept was validated through the historical period and testing was carried out using water level observations for 2022 and 2023. Testing showed that extreme events remain challenging to predict, especially when they fall outside of the bounds of historical observations. Ongoing model refinement aims to improve accuracy to provide more reliable information to decision makers and emergency management organizations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".