Carbon storage in tidal marsh sediments in the Bay of Fundy : the role of vegetation and depth
Bibliographic record
Abstract
Tidal marshes have the ability to sequester and store atmospheric CO2 and thus contribute a valuable ecosystem service.Globally, tidal marshes have declined due to environmental damage and habitat conversion-however, restoration has become a promising mode of revitalization of these ecosystems.Little is known about carbon storage differences between restored and natural marshes or the factors that influence carbon storage in these systems.This study compares belowground carbon stocks in three tidal marshes (new restoration, old restoration, natural reference).Carbon content was sampled using a Russian peat corer at three locations in Spartina alterniflora vegetation at each marsh.Two sediment cores were taken at each sampling location, one from an area with live plants and one from bare mud, and each core subdivided into three depths: surface (<3cm), rhizosphere (3cm-30cm) and below-rhizosphere (<30cm).Statistical analysis showed that depth had no significant effect.Given this, it appears that the depth at which carbon is buried does not impact long-term carbon storage within tidal marshes.The older restoration and natural sites contained a similar amount of buried carbon as the new restoration site.There was no significant difference in carbon storage between vegetated versus unvegetated areas across all marshes.Further studies should explore the role of sedimentation and its influence on carbon storage within these systems.In addition, the impact of climate change should also be monitored within tidal marshes to ensure correct methods for conservation and restoration are being employed.accomplish it without the help and guidance of several key individuals and organizations.Firstly, I would like to thank Dr. Jeremy Lundholm and the Ecology of Plants and Communities (EPIC) lab as without this foundation, I would not have been able to complete my research.Dr. Lundholm, your patience and kind-nature have been much appreciated throughout this process and I could not have had a better supervisor.Special thanks to Kendra Sampson for her help in the field-I would not have made it through the hot summer days collecting sediment cores without your motivation and spirit-and Erin Cameron, my reader for her positive feedback and critiques.I would like to
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".