Modeling Subsurface Drainage in Compacted Cultivated Histosols
Bibliographic record
Abstract
Reclaiming histosols in Montéregie region, Québec, Canada, increases peat decomposition and compaction rate and decreases the effectiveness of subsurface drainage. The objective of this paper was to use HYDRUS-2D to model the behavior of subsurface drainage systems, in order to evaluate the compaction effect on drain depth and spacing, and to determine the compact layer thickness and saturated hydraulic conductivities (Ksat) resulting in an improvement of subsurface drainage]. The drainage model was calibrated [Nash-Sutcliffe efficiency coefficient (NSE) = 0.958, percent bias (PBIAS) = −0.57%] using Ksat, meteorological data, and matric potential (h) data measured on the project site from June 10 to July 19, 2017. The calibrated and validated model was used to analyze the variation of h values (Δh in cm d−1) as a function of drain spacing (2–7 m) and drain depth (1 and 1.2 m) and to identify the response surface of Δh to various compact layer thickness and Ksat combinations. The results showed that Δh was on average 58% greater below the compact layer than above it and that reducing drain spacing or increasing drain depth does not improve the drainage rate. The analysis of the compact layer thickness and Ksat effect on Δh showed that for a Δh of 40 cm d−1, Ksat actual values in the two uppermost layers should be multiplied by 50 for compact layer thickness varying from 12 to 35 cm. Water percolation in the soil is reduced by the compact layer. Soil management methods for improving Ksat should therefore be better than deepening the drains or and reducing the spacing.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".