Widespread surface water<i>p</i>CO2 undersaturation during ice-melt season in an Arctic continental shelf sea (Hudson Bay, Canada)
Bibliographic record
Abstract
Estimating sea–air CO2 fluxes in coastal seas remains a source of uncertainty in global carbon budgets because processes like primary production, upwelling, water mixing, and freshwater inputs produce high spatial and temporal variability of CO2 partial pressure (pCO2). As a result, improving our pCO2 baseline observations in these regions is important, especially in sub-Arctic and Arctic seas that are experiencing strong impacts of climate change. Here, we show the patterns and main processes controlling seawater pCO2 and sea–air CO2 fluxes in Hudson Bay during the 2018 spring and early summer seasons. We observed spatially limited pCO2 supersaturation (relative to the atmosphere) near river mouths and beneath sea ice and widespread undersaturated pCO2 in offshore and ice-melt-influenced waters. pCO2 was highly correlated with salinity and temperature, with a limited but statistically significant relationship with chlorophyll a and fluorescent dissolved organic matter. Hudson Bay on average was undersaturated with respect to atmospheric CO2, which we attribute mainly to the dominance of sea-ice meltwater. We calculated an average net CO2 flux of about –5mmol CO2 m–2 day–1 (–3.3 Tg C) during the spring and early summer seasons (92 days). Combining this result with extrapolated estimates for late summer and fall seasons, we estimate the annual CO2 flux of Hudson Bay during the open water season (184 days) to be –7.2 Tg C. Our findings indicate that the bay on average is a weaker CO2 sink than most other Arctic seas, emphasizing the importance of properly accounting for seasonal variability in the Arctic coastal shelves to obtain reliable sea–air CO2 exchange budgets.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".