Snapshots of ice-free season dynamics in the near-shore water column of the northern Gulf of St. Lawrence, Canada
Bibliographic record
Abstract
Abstract. Coastal ecosystems are highly dynamic and vulnerable to both climate changes and anthropogenic pressures. The Sept-Îles region, located in the northwestern Gulf of St. Lawrence, is a high-use subarctic coastal system with diverse urban, industrial and maritime activities. This study presents analyses of monthly water column profiles at 35 sites focusing on temperature, salinity and chlorophyll fluorescence, used as a proxy of phytoplankton biomass, during the ice-free season. Using a conductivity, temperature and depth (CTD) sensor, water column profiles were collected from May to October 2022 along the coastline, at sites between 2- and 52- meters depth. Results revealed a thermocline developing in spring, intensifying in summer and disappearing in autumn. Chlorophyll a (Chl a) concentrations peaked below the thermocline in July, while secondary increases were recorded at the surface in September, consistent with observations of an autumn bloom in similar environments. These findings highlight the complex dynamic of physical and biological parameters in the coastal water column, and the importance of the timing of sampling to fully capture seasonal variability. To improve future research in the area, measuring nutrient concentrations would be essential for detecting potential upwelling events and better explaining phytoplankton variation during summer. This study provides a valuable baseline for future investigations and justifies the continuation of measurements of water column variability in the region, in the context of rapid climate change. The complete dataset is available via https://doi.org/10.5683/SP3/ALRWON (Arseneault & Saulnier-Talbot, 2025a).
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".