Investigating peat hydrological properties using field and laboratory methods: application to the Lanoraie peatland complex (southern Quebec, Canada)
Bibliographic record
Abstract
Abstract Flow dynamics within a peatland are governed by hydraulic parameters such as hydraulic conductivity, dispersivity and specific yield, as well as by anisotropy and heterogeneity. The aim of this study is to investigate hydraulic parameters variability in peat through the use of different field and laboratory methods. An experimental site located in the Lanoraie peatland complex (southern Quebec, Canada) was used to test the different approaches. Slug and bail tests were performed in piezometer standpipes to investigate catotelm hydraulic conductivity. Combined Darcy tests and tracer experiments were conducted on cubic samples using the modified cube method (MCM) to assess catotelm hydraulic conductivity, anisotropy and dispersivity. A new laboratory method is proposed for assessing acrotelm hydraulic conductivity and gravity drainage using a laboratory experimental tank. Most of slug tests' recovery curves were characteristic of compressible media, and important variability was observed depending on the initial head difference. The Darcy experiments on cubic samples provided reproducible results, and anisotropy (Kh > Kv) was observed for most of samples. All tracer experiments displayed asymmetrical breakthrough curves, suggesting the presence of retardation and/or dual porosity. Hydraulic conductivity estimates performed using the experimental tank showed K variations over a factor of 44 within the upper 40 cm of the acrotelm. The results demonstrate that the intrinsic variability associated with the different field and laboratory methods is small compared with the spatial variability of hydraulic parameters. It is suggested that a comprehensive assessment of peat hydrological properties can be obtained through the combined use of complementary field and laboratory investigations. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".