Sensitivity of freshwater dynamics to ocean model resolution and river discharge forcing in the Hudson Bay Complex
Bibliographic record
Abstract
Hydroelectric development and regulation have modified the temporal and spatial distribution of runoff entering the Hudson Bay Complex (HBC). To understand the impacts and future of regulation in this region, the numerical ocean model, NEMO, run with the Arctic and Northern Hemispheric Atlantic (ANHA) configuration, is used to model present day freshwater dynamics associated with river runoff and sea ice melt. The present work establishes the freshwater budget in each subregion of the HBC, in addition to evaluating the sensitivity to model resolution and estimates of river discharge forcing. It is shown that the annually averaged HBC freshwater budget is mainly a balance between river discharge and freshwater advected out of the region, while surface fluxes (ice melt and growth, and precipitation and evaporation) are the dominant term on seasonal time scales. Runoff forcing is found to impact the long term mean volume and freshwater fluxes out of the HBC, while increased resolution has minimal effect on these fluxes, with the exception of the Southampton–Baffin Island gate. Quantitative estimates of turbulent, mean, and Ekman components of freshwater exchange between the interior and boundary regions of Hudson Bay are also presented. We use offline Lagrangian passive tracers to estimate the HBC runoff residence time, which is as long as 32 years.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".