Hydroclimatic controls on water balance and water level variability in Great Slave Lake
Bibliographic record
Abstract
Abstract On average, 86% of riverine discharge to Great Slave Lake, Northwest Territories, Canada, was gauged during the period 1964–1998, offering an unprecedented opportunity to study and understand controls on water balance of a large northern lake at the headwaters of the Mackenzie River. A functional daily water balance model, incorporating measurements of riverine inflow, precipitation on the lake surface, evaporation, and riverine outflow was developed, which predicts the amplitude and frequency of annual water level fluctuations, and closes the water balance to within ± 6% for 28 of 35 years and ± 11% for the remaining 7 years, with an overall systematic error of + 2%. Annual water balance estimates for the period 1964–1998 reveal that about 74% of inflow into Great Slave Lake originates from the Peace‐Athabasca catchments that enter the lake via the Slave River, whereas 21% is derived from other catchments bordering Great Slave Lake, and 5% from precipitation on the lake surface. An estimated 94% of water losses occur by riverine outflow to the Mackenzie River and 6% by evaporation from the lake surface. The primary driving force behind water level fluctuations in Great Slave Lake, including the post‐regulation period following development of the W.A.C. Bennett Dam, is shown to be climate‐driven precipitation variability in the Peace‐Athabasca basins. A simple precipitation regression model is developed to simulate water level fluctuations in Great Slave Lake over the past 100 years. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".