Influence of pore structure on solute transport in degraded and undegraded fen peat soils
Bibliographic record
Abstract
In peat soils, decomposition and degradation reduce the proportion of large pores by breaking down plant debris into smaller fragments and infilling inter-particle pore spaces. This affects water flow and solute migration which, in turn, influence reactive transport processes and biogeochemical functions. In this study we conducted flow-through reactor experiments to investigate the interplay between pore structure and solute transport in samples of undegraded and degraded peat collected in Canada and Germany, respectively. The pore size distributions and transport parameters were characterised using the breakthrough curve and two-region non-equilibrium transport model analyses for a non-reactive solute. The results of transport characterisation showed a higher fraction of immobile pores in the degraded peat with higher diffusive exchanges of solutes between the mobile and immobile pores associated with the dual-porosity structure. The rates of steady-state potential nitrate reduction were compared with pore fractions and exchange coefficients to investigate the influence of pore structure on the rates of nitrate reduction. The results indicated that the degraded peat has potential to provide the necessary boundary conditions to support nitrate removal and serves as a favourable substrate for denitrification, due to the nature of its pore structure and its lower organic carbon content compared to undegraded peat.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".