Controls on organic carbon stocks among restored wetland soils in the Long Point region of southern Ontario, Canada
Bibliographic record
Abstract
Freshwater marsh restoration can be a viable natural climate solution; however, the extent to which marsh soils bury and preserve organic carbon within policy‐relevant timescales remains highly uncertain. Here, we compare organic carbon masses and accumulation rates from an undrained reference marsh, a passively restored freshwater marsh (reflooded after 1954) and a chronosequence of actively restored freshwater marshes (<10 years in age) situated in Lake Erie watersheds in the Long Point Biosphere Reserve of Ontario, Canada. The reference site has sustained the highest rates of short‐term organic carbon accumulation (235 g C m−2 yr−1) over the last four decades and has the highest mass of soil organic carbon (122 tC/ha) at 0–30 cm depth. Organic carbon masses are highly variable among all restored wetlands (16–115 tC/ha) at 0–30 cm depth and are not strongly related to time since restoration at least over the last 10 years. Nonetheless, we show that passive wetland restoration generates high rates of organic carbon accumulation (144 g m−2 yr−1) on a multidecadal scale where sites are low‐lying, underlain by alluvial deposits and connected to larger ground and surface water networks. Active restoration measures (e.g. excavation, installation of berms) may promote organic carbon preservation, particularly where fine‐grained soil texture promotes waterlogging. We demonstrate the importance of substrate, topographic gradient, and hydrology in selecting sites for marsh restoration to maximize carbon sequestration, and argue that the presettlement context and reference paleorecords provide necessary baselines for directing successful wetland restoration.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".