Sediment sources and transport dynamics in large, regulated river systems with multiple lakes and reservoirs in the subarctic region of Canada
Bibliographic record
Abstract
Abstract The Burntwood River (BR) and Upper Nelson River (UNR) are regulated rivers in the subarctic region of Canada. They merge at Split Lake and then discharge into Hudson Bay via the Lower Nelson River (LNR). The BR water discharge was increased eight‐fold by a cross‐watershed diversion in 1976. The UNR drains the 11 th largest lake in the world, Lake Winnipeg, which itself receives discharge from a large North American Interior Plains watershed. Sediment loads and the source fingerprinting approach in these rivers were used to: (a) identify the sediment sources; (b) examine the impact of climate change and flow regulation on the BR and UNR sediment loads; and (c) assess the influence of Split Lake on downstream delivery of sediment into the LNR. Lake Winnipeg effectively decouples the UNR from the sediment sources in its prairie watershed. Fluvial riverbank and reservoir shoreline erosion in the UNR increased in the late 1990s, in response to a multi‐decadal increase in discharge forced by climate change in the Lake Winnipeg watershed. The BR sediment load was increased seven‐fold by diversion. Since diversion, flow regulation near the licenced limit has muted the response to variability in local precipitation and runoff; however, erosion processes independent from discharge (bank failures and subaerial processes) add variability in the sediment load record. Based on sediment budgeting, Split Lake conveys almost 80% of the BR and UNR sediment load into the LNR. The greater sediment load in the UNR (~1100 Gg year −1 , compared to ~530 Gg year −1 from the BR) reveals that the UNR is the primary sediment source into the LNR, so that downstream sediment transport dynamics are more sensitive to the environmental changes in the UNR than to disturbances in the BR. Whether this may change in the future depends on changes in climate and engineering responses to increasing demand for hydroelectric power.
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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 | 0.000 |
| 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) | 0.000 | 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".