Long-Term Carbon Accumulation in Temperate Swamp Soils: A Case Study from Greenock Swamp, Ontario, Canada
Bibliographic record
Abstract
Abstract Wetlands that develop peat are a globally significant pool of soil carbon. While some wetland types such as bogs and fens are well characterized by the consistent development of carbon-rich peat, swamps soils are more variable both in terms of their carbon densities and accretion rates. Subcategorizing swamps by forest type may be a useful way of understanding this variability. Here we provide a case study of carbon accumulation in two distinct forest stands of Greenock Swamp located in the Great Lakes – St Lawrence mixed forest region in Bruce County, Ontario, Canada: an Acer - Fraxinus (maple-ash) dominant stand (i.e., broad-leaved swamp) and a Thuja occidentalis (cedar) dominant stand (i.e., needle-leaved swamp). Organic matter and organic carbon contents were analyzed among seven Acer - Fraxinus soil cores and one Thuja occidentalis core collected from Greenock Swamp. The Acer - Fraxinus cores had peat depths ranging from 18-60 cm with a mean organic matter content of 54% and an organic carbon content of 34% of dry mass. The Thuja occidentalis swamp core had at least 4 m of almost homogeneous peat with a mean organic matter content of 89%. Radiocarbon dating indicates that the Acer - Fraxinus swamp accumulates peat episodically whereas the Thuja occidentalis swamp showed continuous peat accumulation since the Middle Holocene. Overall, both broad-leaved and needle-leaved swamps have important soil carbon stocks, and are prevalent wetland types in middle latitudes. Both need to be considered to fully represent the carbon pools and potential sink of temperate wetlands.
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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 | 0.003 |
| Science and technology studies | 0.004 | 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".