Model study of the impact of hydropower developments on the oceanography of Hudson Bay
Bibliographic record
Abstract
Hudson Bay is a large inland sea in northern Canada which is characterized by high tides and strong residual currents. It is relatively shallow and isolated from the ocean, and its physical oceanography is largely dependent on freshwater river runoff, surface wind, freshwater and heat fluxes. The freshwater budget of Hudson Bay has a substantial impact on the environment of the basin, its salinity, stratification of the water column and sea-ice formation. The export of fresh surface waters via the Hudson Strait into the Labrador Sea have been a center of intense studies because of their potential effect on the vertical stratification and deep convection in the Northwest Atlantic Ocean. Over the past several decades, some of the largest rivers which discharge into Hudson Bay (Nelson, Churchill, Moose, and La Grande Riviere) have been affected by dams, diversions, and reservoirs constructed for generation of hydroelectricity. The thesis presents results from a model study of the impact of this development on the oceanography of Hudson Bay. I use an eddy-permitting, non-tidal model of the North Atlantic and Hudson Bay forced with NCEP atmospheric forcing over the period from 1948 up to 2005. River run-off is determined based on Environment Canada data for 23 rivers which discharge into the HBS collected between 1964 and 2005. The model results suggest that the hydropower developments in the mid-1970s had two major effects on the characteristics of river runoff into Hudson Bay. Firstly, they reduced the amplitude of seasonal cycle of the freshwater input of some major rivers. Secondly, they caused a change in the spatial distribution of annual mean river runoff. The river diversions had a significant impact on the ocean characteristics of the James Bay. The model simulations suggest that the surface salinity in this region increased since the mid-1970s also affecting processes of vertical mixing and ice-formation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".