Spatiotemporal variability in pH and carbonate parameters on the Canadian Atlantic continental shelf between 2014 and 2022
Bibliographic record
Abstract
Abstract. The Atlantic Zone Monitoring Program (AZMP) was established by Fisheries and Oceans Canada (DFO) in 1998 with the aim of monitoring physical and biological ocean conditions in Atlantic Canada in support of fisheries management. Since 2014, at least two of the carbonate parameters (pH; total alkalinity, TA; and dissolved inorganic carbon, DIC) have also been systematically measured as part of the AZMP, enabling the calculation of derived parameters (e.g., carbonate saturation states, Ω, and partial pressure of CO2, pCO2). The present study gives an overview of the spatiotemporal variability in these parameters between 2014 and 2022. Results show that the variability in the carbonate system reflects changes in both physical (e.g., temperature and salinity) and biological (e.g., plankton photosynthesis and respiration) parameters. For example, most of the region undergoes a seasonal warming and freshening. While the former will tend to increase Ω, the latter will decrease both TA and Ω. Spring and summer plankton blooms decrease DIC near the surface and then remineralize and increase DIC at depth in the fall. The lowest pCO2 values (down to ∼ 200 µatm) are located in the cold coastal Labrador Current, whereas the highest values (>1500 µatm) are found in the fresh waters of the Gulf of St. Lawrence and the St. Lawrence Estuary. The latter is also host to the lowest pH values of the zone (7.48 in the fall of 2022). Finally, most of the bottom waters of the Gulf of St. Lawrence (>90 %) are undersaturated with respect to aragonite (Ωarg<1). In addition to providing a baseline of carbonate parameters for the Atlantic Zone as a whole, this comprehensive overview is a necessary and useful contribution for the modelling community and for more in-depth studies. The full dataset of measured and derived parameters is available from the Federated Research Data Repository: https://doi.org/10.20383/102.0673 (Cyr et al., 2022a).
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".