Impact of Pore-Size-Class on Carbon Turnover in Peat Soils
Bibliographic record
Abstract
The loss of carbon from peatlands occurs through gaseous emissions and a substantial fraction from aquatic fluxes, specifically dissolved organic carbon (DOC), during mineralisation and degradation processes. Our study hypothesises that DOC production is dependent on pore size, with higher concentrations occurring in finer pores. To investigate this, pore water was extracted at specific pressure heads (-60 and -600 hPa), representing macro- and mid-size pore domains, from degraded peat samples. Soil organic matter content was measured at 34 wt% in the topsoil and 57 wt% in the subsoil. Notably, the more degraded topsoil exhibited significantly higher average DOC concentrations than the subsoil, with levels 1.5 times greater at -60 hPa and 2.4 times higher at -600 hPa. These trends indicate that degraded peat soils are prone to release greater amounts of DOC. Additionally, DOC concentrations in topsoil samples were consistently higher at -600 hPa compared to -60 hPa.The negative correlation between soil organic matter (SOM) and DOC at -600 hPa (r = - 0.53; p < 0.0001) aligns with degradation-driven reductions in SOM and porosity. Degraded topsoil exhibited high DOC variability for SOM < 40 wt%, stabilising below 50 mg/L for SOM ≥ 40 wt%. Through a graphical illustration, we infer that the elevated DOC export is likely due to the higher surface-to-volume ratio observed in mid-sized pores (-60 to -600 hPa), further enhanced by the dual-porosity structure of the degraded topsoil. This structural variation contributes to differences in carbon turnover rates. Additionally, microbial communities and their abundance differ across pore size classes, causing pore size-dependent reactions that influence DOC export.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".