Streamflow input to Lake Athabasca, Canada
Bibliographic record
Abstract
Abstract. The 271 000 km2 Lake Athabasca drainage in Northern Canada encompasses ecologically-rich and sensitive ecosystems, intensive agricultural lands, vast forests, glacier-clad mountains, and abundant oil reserves in the form of tar-sands. In this study, streamflow variability and trends in eight rivers feeding the 7800 km2 Lake Athabasca are investigated over the period 1960–2010. Hydrological regimes and trends are established using a robust regime shift detection method and the Mann-Kendall (MK) test, respectively. Results show that the Athabasca River, which provides ~ 57% of the total annual lake inflow of 34.06 km3 yr−1, experiences marked declines in recent decades impacting lake levels and its ecosystem. The Fond du Lac River, which contributes ~ 30% of total Lake Athabasca inflow, has an increasing trend of 0.021 km3 yr−1 over 1970–2010 according to the MK test, equating to a 0.86 km3 discharge increase from Fond du Lac River to the lake. From 1960 to 2010 there has been approximately a 21.2% reduction of average discharge equivalent to a 7.22 km3 recession in the Lake Athabasca causing lake levels to drop. The lake level has a trend of −0.008 m yr−1 which is equivalent to a 0.39 m decline in the lake level over 1960–2010. The total lake inflow trend over 1977–2010 is −0.207 km3 yr−1 or a reduction of 25.67 km3 by 2100 by linear extrapolation. This may imply a further reduction of 2 m to 3 m in lake level that is in the range of a 5200-yr historical minimum inferred from proxy data in nearby sediment cores.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".