Gulf of St. Lawrence and Estuary Dataset (GOSLED): A 20-Year Compilation of Quality-Controlled Biogeochemical Observations (2003–2023)
Bibliographic record
Abstract
This dataset includes 20 years (2003–2023) of biogeochemical observations collected during 21 dedicated research cruises in the Estuary and Gulf of St. Lawrence as well as its main tributary, the Saguenay Fjord. The dataset contains CTD sensor measurements of pressure, temperature, practical salinity, and dissolved oxygen, as well as discrete oxygen measurements determined by Winkler titration. Additionally, the dataset includes discrete carbonate-system parameters such as dissolved inorganic carbon, total alkalinity, pH (total proton scale), and the fugacity of CO₂, along with macronutrient concentrations (nitrate, nitrite, ammonium, soluble reactive phosphate, and silicate). Organic parameters such as dissolved organic carbon and total nitrogen are included, as are the concentrations of selected biogeochemical gases (e.g., nitrous oxide) and the stable carbon isotopic composition of the DIC and DOC, as well as the δ¹⁸O and δD of H₂O. Measured transient tracers include CFC-12 and SF₆ and a deliberate tracer, CF₃SF₅. Data were compiled from both historical measurements (2003–2020) and recent sampling efforts from the MEOPAR–RQM TReX project (2021–2023) and RQM - Odyssée Saint Laurent program (2018-2023). All data were processed following primary quality control procedures adapted from GLODAP and CODAP-NA standards. Secondary crossover analysis was not possible due to a lack of deep-water sampling. This dataset is a rare, quality-controlled biogeochemical time series for the Gulf and St. Lawrence Estuary and provides a baseline for future research on deoxygenation, acidification, and nutrient cycling in Eastern Canada. For more information about the dataset, see the data description article published in Earth System Science Data (ESSD): Gulf of St. Lawrence and Estuary Dataset (GOSLED): a 20-year compilation of quality-controlled biogeochemical observations (2003–2023), https://doi.org/10.5194/essd-18-3609-2026.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".