Derivation of physically based soil hydraulic parameters in New Zealand by combining soil physics and hydropedology
Bibliographic record
Abstract
Abstract Field‐characterised soil morphological data (to 1 m depth) and modelled soil water release characteristics are recorded in the S‐map database for soils covering approximately 40% of New Zealand's soil area. This paper shows the development of the Smap‐Hydro database that estimates hydraulic parameters by synergising soil morphologic data recorded in S‐map and soil physics. The Smap‐Hydro parameters were derived using the bi‐modal Kosugi hydraulic function. The validity of the Smap‐Hydro parameters was tested by applying them within an uncalibrated physically based hydrological model (HyPix) and comparing results with soil water content, θ , measured with Aquaflex soil moisture probes (0–40 cm deep) at 24 sites across New Zealand. The HyPix model provided an excellent fit with observed soil water content for 25% of the sites, a good fit for 33% of the sites and a poor fit for 42% of the sites. Applying the model to all soils in the S‐map database required adjustments for the occurrence of rock fragments, hydraulic discontinuities caused by soil pans and required the addition of boundary conditions for water tables and the occurrence of impermeable rock. A discussion on how we can further synergise the development of pedotransfer functions with knowledge of soil physics is provided.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".