Sedimentary processes in large, regulated river systems in the Canadian subarctic
Bibliographic record
Abstract
The goal of this thesis is to develop an improved understanding of sedimentary processes along the Nelson River system in the subarctic region of Canada. Sediment sources and transport dynamics in Lake Winnipeg, the Upper Nelson River (UNR) – between Lake Winnipeg and Split Lake – as well as the Burntwood River (BR) – the major tributary of the Nelson River in the subarctic region – were investigated. The properties of Lake Winnipeg’s bottom sediment and the lake’s total sediment budget were used to provide a better understanding of: a) the sedimentation dynamics in Lake Winnipeg; and b) its role in sediment transport in the Nelson River system. The sediment source fingerprinting technique and long-term record of sediment load data on the BR and the UNR were used for separating the importance of climate change from human-induced environmental changes on these two regulated rivers. Moreover, the influence of Split Lake on the downstream delivery of sediment to the Lower Nelson River and Hudson Bay was investigated by developing the sediment budget for this riverine lake. In addition, the collection of a representative sample of ambient suspended sediment using a well-established time-integrated sampler and two adapted discrete samplers was investigated. The performance of these samplers was examined in a controlled laboratory and under field conditions. Assessing these samplers was conducted to determine the most suitable device to collect representative bulk samples from the Nelson River system. The results show that the sediment load derived from the prairies area is sequestered in Lake Winnipeg, along with nutrients and contaminants bound to them. Another key finding was that sediment derived from bluff erosion on the northern shore of the lake is the major source for sediment being exported in the lake’s outflow. This thesis also found that the UNR is characterized by increases in sediment loading as a result of climate change. However, in the BR, cross-watershed water diversion caused a seven-fold increase in sediment discharge and since diversion flow regulation near the licenced limit has muted the response to variability in local precipitation and runoff. This thesis also provides several recommendations for further research.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".