A simple field method for estimating the mass of organic carbon stored in undisturbed wetland soils
Bibliographic record
Abstract
We have compiled a large dataset of peat and soil cores from temperate and boreal regions of eastern Canada to develop a simple field method for estimating the mass of soil organic carbon (SOC) stored in undisturbed wetlands (peatlands, swamps and marshes). We show that it is possible to predict the SOC mass in different wetland types by measuring the organic-rich soil layer thickness in the field. Using this new dataset, we found that SOC mass can be estimated either by using the linear regression equation between peat or soil thickness and SOC mass or by multiplying peat or soil thickness by a mean SOC density. We also show that SOC mass can be estimated by determining the degree of peat humification in the northern peatlands investigated. In this dataset, the precision of estimates is higher for peatlands than mineral wetlands (marshes and swamps), mainly due to the lack of empirical soil core data. The simple approach proposed here could be applied in different wetland regions worldwide where carbon density data from soil cores are available. This cost- and time-efficient method could benefit regional or national-scale carbon inventories.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".