Transit time of deep and intermediate waters in the Gulf of St. Lawrence
Bibliographic record
Abstract
• Particle transit is faster in intermediate than in deep gulf of St. Lawrence water. • Particle trajectories are complex and variable. • Presence of multiple gyres lead to longer transit times. • Seasonality influences transit times. The transit time of the subsurface waters in the hypoxic and acidified Gulf of St. Lawrence (GSL) is poorly understood, despite its strong influence on physical and biogeochemical water properties. Three estimates of the transit time of the deep waters between Cabot Strait and the head of the Laurentian Channel, a deep channel cutting through the GSL, have been published up to now. Here, using lagrangian tracking experiments in a regional ocean model, we provide a new estimate of the transit time in the deep layer (> 225 m) of the GSL, as well as the first estimate of the transit time in the intermediate layer (50–175 m). Our estimate for the deep layer is 3.2 ± 0.7 years. The transit time in the intermediate layer (1.2 ± 0.5 years) is nearly three times faster than in the deep layer. The deep waters travel mainly up the Laurentian Channel, whereas most of the intermediate waters first transit through the Esquiman and /or Anticosti Channels. Our results also highlighted the impact of the seasonal changes in large-scale circulation on the transit times of the particles seeded at Cabot Strait. In summer and fall, the circulation is relaxed, and subsurface waters transit slowly but more directly upstream, leading to faster transit times. In winter and spring, the circulation is intensified but many particles get caught in large gyres prevalent during these seasons, leading to slower average transit times.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".