High biodegradability of riverine dissolved organic carbon in late winter in Hudson Bay, Canada
Bibliographic record
Abstract
Hudson Bay, at the southern margin of the Arctic Ocean, receives nearly one-third of Canada’s river discharge and approximately 5.5 Tg of riverine dissolved organic carbon (DOC) annually. Riverine DOC fluxes to Hudson Bay are expected to increase with climate change, but how this increase will influence the biogeochemistry of the coastal waters is largely unknown. In particular, the fate of riverine DOC that enters Hudson Bay during the dark, frozen winter period (roughly January to April) is poorly known despite high discharge from the large, regulated rivers of Hudson and James Bays at that time. Few studies have assessed the degradability of riverine DOC transported in winter anywhere across the Arctic, leaving unanswered questions regarding the impact of riverine DOC on the Arctic carbon budget, CO2 fluxes, and local food webs. Here, we assessed the biodegradability of DOC in riverine and coastal waters of southern Hudson Bay in late winter using 45-day incubation experiments. We found 24%–60% of the DOC in the rivers and on average 21% of the DOC in the immediate coastal waters to be biodegradable. Differences in biodegradability appeared to depend on properties of the rivers/watersheds and physical and biochemical processes in the aquatic environments. DOC biodegradability correlated strongly with DOC concentration, which was higher during winter than summer in all studied rivers and higher in the Nelson and Hayes Rivers, draining the Hudson Bay Lowlands than in most previously studied large rivers of the Arctic watershed. The Nelson River, regulated for hydropower production, had the highest winter DOC concentrations and most degradable DOC. The high biodegradability of Hudson Bay riverine DOC in late winter and high concentrations and fluxes of riverine DOC at that time imply strong leverage for future increases in DOC fluxes to impact the carbon cycle of these coastal waters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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 teacher head, 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".