Pore-Size-Class Dependent Carbon Turnover in Peat Soils
Bibliographic record
Abstract
Carbon loss from peatlands involves both gaseous emissions and a significant contribution from the water-bound fraction, specifically dissolved organic carbon (DOC), during mineralization and degradation processes. Our hypothesis proposes that DOC production is dependent on pore size, with elevated concentrations occurring in finer pores. To test this hypothesis, we extracted pore water at well-defined pressure heads (-60 and -600 hPa), representing macro- and mid-size pore domains, in degraded peat samples. Topsoil and subsoil samples exhibited soil organic matter contents of 34wt% and 57wt%, respectively. Remarkably, the more degraded topsoil consistently displayed significantly higher average DOC concentrations than the subsoil, with 1.5 times greater levels at -60 hPa and 2.4 times higher at -600 hPa. This trend suggests that more degraded peat soils are prone to releasing higher amounts of DOC. Furthermore, in topsoil samples, DOC concentrations were consistently higher at the -600 hPa pressure head compared to -60 hPa. To enhance our understanding, we computed hydraulic conductivities at -60 and -600 hPa using Van Genuchten parameter values, subsequently estimating the DOC load under unit gradient conditions. This calculation is particularly relevant for real-field situations, especially in partially saturated (degraded) peat soils. The hydraulic conductivity at -600 hPa was nearly a hundred times lower than at -60 hPa, leading to the conclusion that macro-pores serve as the primary pathways for DOC release in peat soils, irrespective of higher DOC concentrations in the fine pore domain.
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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 | 0.000 | 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) | 0.000 | 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".