Spatial variability in surface-water pCO2 and gas exchange in the world's largest semi-enclosed estuarine system: St. Lawrence Estuary (Canada)
Bibliographic record
Abstract
The incomplete spatial coverage of CO 2 partial pressure (pCO 2 ) measurements across estuary types represents a significant knowledge gap in current regional-and global-scale estimates of estuarine CO 2 emissions.Given the limited research on CO 2 dynamics in large estuaries and bay systems, as well as the sources of error in the calculation of pCO 2 (carbonic acid dissociation constants, organic alkalinity), estimates of air-sea CO 2 fluxes in estuaries are subject to large uncertainties.The Estuary and Gulf of St. Lawrence (EGSL) at the lower limit of the subarctic region in eastern Canada is the world's largest estuarine system, and is characterized by an exceptional richness in environmental diversity.It is among the world's most intensively studied estuaries, yet there are no published data on its surface-water pCO 2 distribution.To fill this data gap, a comprehensive dataset was compiled from direct and indirect measurements of carbonate system parameters in the surface waters of the EGSL during the spring or summer of 2003-2016.The calculated surface-water pCO 2 ranged from 435 to 765 µatm in the shallow partially mixed upper estuary, 139-578 µatm in the deep stratified lower estuary, and 207-478 µatm along the Laurentian Channel in the Gulf of St. Lawrence.Overall, at the time of sampling, the St. Lawrence Estuary served as a very weak source of CO 2 to the atmosphere, with an area-averaged CO 2 degassing flux of 0.98 to 2.02 mmol C m -2 d -1 (0.36 to 0.74 mol C m -2 yr -1 ).A preliminary analysis revealed that respiration (upper estuary), photosynthesis (lower estuary), and temperature (Gulf of St. Lawrence) controlled the spatial variability in surface-water pCO 2 .Whereas we used the dissociation constants of Cai and Wang (1998) to calculate estuarine pCO 2 , formulations recommended for best prac-tices in open ocean environments may underestimate pCO 2 at low salinities, while those of Millero (2010) may result in overestimates.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".