Modelling Soil Water Retention for Weed Seed Germination Sensitivity to Water Potential
Bibliographic record
Abstract
Soil water retention is important for the study of water availability to germinating weed seeds. Six soil water retention models (Campbell, Brooks-Corey, four- and five-parameter van Genuchten, Tani, and Russo) with residual soil water parameter derivations were evaluated to describe water retention for weed seed germination at minimum threshold soil water potential for three hillslope positions. The Campbell, Brooks-Corey, and four-parameter van Genuchten model with modified or estimated forms of the residual parameter had superior but similar data fit. The Campbell model underestimated water retention at a potential less than −0.5 MPa for the upper hillslope that could result in underestimating seed germination. The Tani and Russo models overestimated water retention at a potential less than −0.1 MPa for all hillslope positions. Model selection and residual parameter specification are important for weed seed germination by representing water retention at the level of minimum threshold water potential for germination. Weed seed germination models driven by the hydrothermal soil environment rely on the best-fitting soil water retention model to produce dynamic predictions of seed germination.
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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 | 0.001 |
| 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 | 0.000 | 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".