Flow alteration impacts on <scp>Hudson Bay</scp> river discharge
Bibliographic record
Abstract
Abstract This study explores flow regulation controls on daily river discharge variations and trends into Hudson Bay from four highly regulated and 17 moderately regulated/unregulated systems over 1960–2016. These 21 rivers contribute ~70% of the total annual riverine freshwater export to Hudson Bay, with highly regulated and moderately regulated/unregulated rivers accounting for 47% and 53% of the discharge, respectively. Daily observed streamflow data from the Water Survey of Canada, Manitoba Hydro, Ontario Power Generation, and Hydro‐Québec are used. Decadal hydrographs of the mean and coefficient of variation of daily river discharge are developed to assess the changing hydrological regimes in both systems. Decadal spectral analyses reveal the dominant controls on daily river discharge input to Hudson Bay from the regulated and unregulated systems. Apart from expected peaks in spectral power on annual timescales arising from the nival regimes in both systems, a strong secondary peak emerges at weekly timescales from flow regulation due to hydropower production. Hydrographs that consider the day of the week reveal distinct weekly cycles in regulated rivers with ~10% declines in daily river discharge during weekends and statutory holidays relative to weekday averages, demonstrating the importance of regulation on the timing of freshwater into Hudson Bay.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".