Water renewals in the<scp>S</scp>aguenay<scp>F</scp>jord
Bibliographic record
Abstract
Abstract Water renewals and renewal times of the Saguenay Fjord are investigated and classified according to their intrusion depth. Renewal dynamics are controlled by a shallow sill (∼20 m) at the fjord mouth, by large tides that are a distinguishing feature of the Saguenay Fjord and by large vertical mixing inside the inner basin ( ). A mooring was deployed in the inner basin of the fjord to provide a clearer quantitative understanding of the complexity and seasonality of water renewals in this seasonally ice‐covered fjord. The mooring provided information on currents over nearly the entire water column, along with temperature‐salinity at a few discrete depths. Hydrographic temperature and salinity transects spanning multiple seasons and years as well as turbulence profiles were also collected. The observations show that the fjord dynamics are more complex than previously hypothesized, with large changes in renewal event depths leading to three different renewal regimes. Part of this renewal depth variability may be explained by the seasonality of the St. Lawrence estuarine circulation. Because of the large turbulence within the inner basin bottom layer, the density decreases over time such that new deep renewals can occur every year. The mechanisms behind the large vertical mixing cannot yet be clearly identified but a statistically significant correlation ( ) suggests that internal wave breaking may be a significant contributor to deep turbulence mixing in the inner basin. The renewal time of the inner basin waters is estimated to be between 1 and 6 months.
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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 | 0.000 |
| 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 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".