Characterization of Water Retention Curves for a Series of Cultivated Histosols
Bibliographic record
Abstract
Water retention curves are essential for the parameterization of soil water models such as HYDRUS. Although hydraulic parameters are known for a large number of mineral and natural organic soils, our knowledge on the hydraulic behavior of cultivated Histosols is rather limited. The objective of this study was to derive characteristic water retention curves for a large cultivated peatland with lettuce ( Lactuca sativa L.) and vegetable farming in southern Quebec, Canada. A comparison showed that the van Genuchten model fits better to the water retention data obtained with a Tempe pressure cell experiment than the Groenevelt–Grant model in terms of residual sum of squares; however, the difference in performance was quite small due to the high number of iterations used for fitting. Finally, an agglomerative cluster analysis of 85 peat samples allowed us to define two distinct water retention curves, where the first water retention curve described samples of relatively shallow (<150 cm) Histosols with an organic content <0.89 and a bulk density >0.3 g cm −3 , and the second curve characterized samples of the deepest (depth 150–230 cm) Histosols with an organic content of up to 0.97 and a bulk density >0.3 g cm −3 , which are the soils that suffered a more dramatic transformation as a result of agriculture. This characterization allows for a multitude of applications, including parameterization of the HYDRUS model for soil water movement, and presents an essential tool for the optimization of water management in cultivated peatlands.
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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 | 0.001 | 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".