Analysis of GRACE-derived terrestrial water storage anomaly trends in the Mackenzie River Basin, Canada
Bibliographic record
Abstract
<!--!introduction!--> Great Slave Lake (GSL), located within the Mackenzie River Basin (MRB) in the Northwest Territories, Canada, is one of the deepest (over 600m) freshwater lakes in the world. Large lakes serve as both an indicator of the impact of climate change on regional hydrological dynamics and as a thermal feedback mechanism that may buffer or exacerbate climate change. In summer 2020, GSL levels reached record highs since gauging began in the 1930s, driven by above-average precipitation across the MRB, especially in the Athabasca and Peace River subbasins, and potentially increased permafrost degradation. Recent studies in this area indicate an overall declining secular trend in terrestrial water storage anomalies (TWSA). The objective of this research is to examine in more detail the TWSA in this region, in order to comprehend the underlying sources for the observed trend. The GRACE/FO level-3 mascon product released by the Jet Propulsion Laboratory was evaluated over two decades (April 2002 to March 2022) and data gaps were filled using automated machine learning to provide a continuous time series. Comparisons with the trends derived from ERA5 total precipitation and streamflow station records indicate that the increasing precipitation feeding GSL is countered by increased surface runoff; despite the positive TWSA observed by GRACE-FO beginning in June 2020, the region is, overall, experiencing a declining trend in terrestrial water storage. Studies such as these provide a more comprehensive understanding of the impact of climate change on the hydrological dynamics of the MRB.
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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 | 0.002 | 0.003 |
| 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".