Modeling soil water dynamics of an intensively cultivated histosol
Bibliographic record
Abstract
Abstract Understanding soil–water dynamics in cultivated organic soils (histosols) is crucial for sustainable agriculture, ecosystem preservation, and climate change mitigation. Data on the soil water retention curves (SWRCs) of these histosols in Canada are not readily available in the literature. The Hydrus‐1D model was used to predict SWRCs for a cultivated organic soil in Quebec. The model was validated with matric potential measured in a soil column at 10, 26, and 48 cm. The optimized hydraulic parameters in the Hydrus model resulted in field capacity moisture contents of 0.6, 0.696, and 0.49 cm 3 cm − 3 at the three depths, respectively. Wilting point moisture contents of 0.165, 0.107, and 0.14 cm 3 cm − 3 were obtained at the same depths. Model accuracy was confirmed with a Nash–Sutcliffe efficiency of 0.76, 0.91, and 0.78 for the three depths, respectively. These findings can inform decisions regarding water management practices such as irrigation and drainage requirements for intensively cultivated organic soils.
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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 | 0.001 | 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".