Inorganic carbon dynamics in the Estuary and Gulf of St. Lawrence: a source or sink of atmospheric CO2 and factors that control the spatial variability in gas exchange
Bibliographic record
Abstract
The incomplete spatial coverage of surface-water CO2 partial pressure (pCO2) data across estuary types represents a significant knowledge gap in current regional- and global-scale estimates of estuarine CO2 emissions. The Estuary and Gulf of St. Lawrence, at the southern limit of the subarctic region in eastern Canada, is the world's largest estuarine system and an excellent analogue of the more general coastal environment, yet no systematic study of its CO2 dynamics has been published to date. pH and total alkalinity measurements are used to calculate the pCO2 in the surface mixed layer that is in active contact with the atmosphere. On the basis of the available data, the area-averaged air-sea CO2 flux is estimated for the whole estuary. The key physical and biogeochemical processes controlling the spatial variability of surface-water pCO2 are then identified, using a further development of the extended optimum multiparameter (OMP) water mass analysis. In so doing, the physically- and biologically-induced changes of the inorganic carbon pool are differentiated.In late spring or early summer 2003–2016, the shallow, partially mixed, river-dominated Upper Estuary was a source of CO2 to the atmosphere due to microbial respiration of organic matter, whereas the deep, stratified, marine-dominated Lower Estuary was a sink of atmospheric CO2 due to net phytoplankton photosynthesis favored by the prevailing environmental conditions. Overall, the large subarctic St. Lawrence Estuary was a very weak source of CO2, with an area-averaged CO2 efflux of 0.98 to 2.02 mmol C m–2 d–1 (0.36 to 0.74 mol C m–2 yr–1) similar to that of strongly stratified and/or marine-dominated systems and Arctic estuaries.
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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 | 0.000 | 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".