The coupled oxygen and carbon dynamics in the subsurface waters of the Gulf and Lower St. Lawrence Estuary and Implications for Artificial Oxygenation
Bibliographic record
Abstract
Abstract. The Gulf and Lower St. Lawrence Estuary have experienced major environmental change over the past century, including the development of hypoxic bottom waters and their simultaneous warming and acidification. Here, we use biogeochemical observations collected during the 2021–2023 TReX project as well as historical data, combined with a tracer-calibrated 1D Advection-Diffusion model with variable boundary conditions to represent dissolved oxygen (DO) and dissolved inorganic carbon (DIC) dynamics within the core of the oxygen minimum zone (27.15–27.3 kg m-3 isopycnals) of the Laurentian Channel. The rate of in-channel oxygen utilization in the deep layer was nearly invariant from 2003 to 2023 at 21.1 ± 2.5 µmol kg-1 yr-1 and the DIC accumulation rate was estimated to be 18.3 ± 2.5 μmol kg-1 yr-1. Using δ13CDIC data, we assess the effect of microbial organic matter remineralization processes and dilution of the 13CDIC pool (−6.6×10-3 ‰ per μmol of added metabolic DIC). These data and the use of a tracer-calibrated model to resolve advection and mixing dynamics reconcile differences in prior estimates of biogeochemical transformation rates. Finally, we apply the model to the mitigation scenario proposed by Wallace et al. (2023) for artificial re-oxygenation of the Laurentian Channel bottom waters using pure oxygen. We estimate that the injection of ~8.3 × 105 tonnes yr-1 of oxygen, equivalent to an additional 55 μmol kg-1 relative to the 2023 boundary concentration proximal to the Cabot Strait, would be required to achieve and maintain above hypoxic levels (>62.5 μmol kg-1) at the head of the Laurentian Channel. Using the model, we estimate the time required to re-establish steady-state along-channel distributions of DO and DIC following a change in offshore boundary conditions to be about 10 years, or twice the along-channel transit time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".