Reducing Uncertainties in Net Carbon Capture to Advance Wetlands as Natural Climate Solutions
Bibliographic record
Abstract
Abstract Wetlands play a crucial role as natural climate solutions (NCS) by sequestering atmospheric carbon dioxide (CO 2 ) in the form of organic carbon (OC) in soils. However, spatial heterogeneity and temporal variability in OC sequestration rates introduce uncertainties that must be addressed to inform climate policy and meet national climate targets. This study integrates expert knowledge with statistical learning techniques to develop localized estimates of OC sequestration rates in wetlands within agricultural landscapes. Experts identified direct process controls—including carbon quantity and quality, cation exchange capacity, aggregate reactivity, redox potential, and air temperature—along with human activities that influence these controls. Using geospatial proxies for these variables, alongside field‐based OC sequestration measurements, we compared and trained statistical learning models that achieved high predictive accuracy (adjusted R 2 ≥ 0.70, MAE ≤ 0.15 Mg C ha −1 yr −1 , RMSE ≤ 0.19 Mg C ha −1 yr −1 ). Variable importance analysis identified wetland inundation probability (a proxy for wetland redox potential), Human Impact Index (a proxy for wetland carbon quantity and quality), and landscape soil properties (proxies for wetland soil texture, cation exchange capacity, and aggregate reactivity) as the most influential predictors explaining the spatiotemporal variation in OC sequestration rates. This study demonstrates that statistical learning models, informed by expert knowledge of process controls, can estimate OC sequestration rates within wetlands, providing potentially critical data to guide policy development and wetland management as effective NCS strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".