Acidification of Lower St. Lawrence Estuary Bottom Waters
Bibliographic record
Abstract
Accumulation of metabolic CO2 can acidify marine waters above and beyond the ongoing acidification of the ocean by anthropogenic CO2. The impact of respiration on carbonate chemistry and pH is most acute in hypoxic and anoxic basins, where metabolic CO2 accumulates to high concentrations. The bottom waters of the Lower St. Lawrence Estuary (LSLE), where persistently severe hypoxia has developed over the last 80 years, is one such case. We have reconstructed the evolution of pH in the bottom waters from historical and recent data, and from first principles relating the stoichiometry of CO2 produced to oxygen consumed during microbial degradation of organic matter. Based on the value of the atmospheric partial pressure of CO2 that best reproduces the preformed dissolved inorganic carbon concentration in the bottom waters, we estimate the average ventilation age of the bottom waters to be 16 ± 3 years. The pH of the bottom waters has decreased by 0.2 to 0.3 over the last 75 years, which is four to six times greater than can be attributed to the uptake of anthropogenic CO2. The pH decrease is accompanied by a decline in the saturation state with respect to both calcite and aragonite. As of 2007, bottom waters in the LSLE are slightly supersaturated with respect to calcite (Ωc ≈ 1.06 ± 0.04) but are strongly undersaturated with respect to aragonite (Ωa ≈ 0.67 ± 0.03).
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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".