Hydro-physical and carbon properties of peat across peatland types and climate zones
Bibliographic record
Abstract
The hydro-physical properties of peat play a pivotal role in regulating the water, nutrient, and carbon cycles of peatland ecosystems. However, our understanding of peat hydraulic properties remains limited, especially at a global perspective. In this study, we compiled a comprehensive global database of the peat physical, hydraulic, and chemical properties, including bulk density (BD), porosity, macroporosity, saturated hydraulic conductivity ( K s ), carbon content, and carbon density, encompassing tropical peatlands, boreal and temperate fens and bogs, and permafrost regions. Our primary objective was to examine how these properties varied along a BD gradient across peatland types and climate zones. The results revealed a robust linear relationship between carbon density and BD for various peatland types with carbon content exceeding 35 % ( R 2 > 0.93, p < 0.001). The carbon density of tropical peatlands was more sensitive to changes in BD than that of boreal and temperate peatlands. Total porosity was found to decrease linearly as BD increased, while macroporosity followed a power-law relationship with BD. These trends were consistent across all peatland types, underscoring a strong and reliable association between BD and both total porosity and macroporosity. Additionally, K s exhibited a general decline with increasing BD, with the relationship characterized by log–log functions that varied among peatland types and climate zones. These findings indicated that hydraulic functions of peat (e.g., carbon density, K s ) were significantly influenced by the peat-forming vegetation such as woody plants, Sphagnum , sedges, and the prevailing climatic conditions of the peatland. This study demonstrated that the key peat hydro-physical–chemical parameters—including carbon density, porosity, macroporosity, and K s could be reliably estimated using the BD, with relatively high coefficients of determination ( R 2 > 0.4), highlighting the critical importance of determining BD as a proxy for estimating other hydro-physical properties of peat when direct measurements are unavailable and potentially serving as reliable tools for estimating the carbon stock of peatlands across peatland types and climate zones.
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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 | 0.002 | 0.002 |
| 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".