Long‐term carbon storage and hydrological control of CO<sub>2</sub> exchange in tundra ponds in the Hudson Bay Lowland
Bibliographic record
Abstract
Abstract Carbon dioxide fluxes and water balance were examined in 43 tundra ponds in the northern portion of the Hudson Bay Lowland near Churchill, Manitoba. Most of the ponds were hydrologically disconnected from their catchments during dry periods throughout the post‐melt season. However, episodic reconnection occurred following large precipitation events where depression storage was exceeded. Significant shifts in pond chemistry were observed following precipitation events, with the degree of CO2 saturation increasing during these periods. Pond CO2 concentrations rapidly fell to pre‐event levels following events, suggesting that hydrological connectivity can affect the magnitude and direction of CO2 gas fluxes in tundra ponds. Atmospheric CO2 invaded ponds with highly organic sediments for most of the summer, suggesting that terrestrially derived inorganic carbon was insufficient to meet the demands of algal net production. In contrast, ponds with highly mineral sediments continued to evade CO2 during the summer. In a subset of 11 ponds, long‐term rates of carbon accumulation in sediment ranged from 0·6 to 2·2 mol C m−2 year−1. Very strong correlations existed between average sediment accumulation rates and pond perimeters and basin areas suggesting that peat may be a major source of sediment carbon. Aeolian transport is also a potentially large source of sediment carbon. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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".