The effect of soil and pasture attributes on rangeland infiltration rates in northern Australia
Bibliographic record
Abstract
Surface runoff is an important factor affecting rangeland pasture productivity and off-site sediment transportation. The application of rangeland biophysical models including sub-models of runoff and erosion provides one method to assess how management and climate variability affect the frequency and quantity of surface runoff events. However, there is often limited confidence in extrapolating runoff models developed from site-specific, hillslope field experiments to other locations due to variation in soil types and land condition states. To improve rangeland runoff models, we investigated three potentially important components at 18 paired land condition sites: (1) the importance of a variety of pasture attributes such as biomass and cover on infiltration rates; (2) the impact of surface soil texture on infiltration rates; and (3) whether soil carbon and/or soil bulk density provide valuable indicators of a site’s infiltration rates. The study found that surface soil texture was important when aboveground biomass was low and was found to have a ‘broken-stick’ relationship with infiltration rates (i.e. lowest infiltration occurred at the pivot point of 64% sand). Aboveground biomass, (which included standing grass, grass litter and tree litter) was the best soil or pasture attribute for predicting a plot’s infiltration capacity accounting for 68% of the variability. Plots with surface soil sand content greater than 60% and which had been exclosed for between 4 and 24 years had higher average surface soil carbon mass and concentration (~10%) than adjacent grazed plots. The exclosed plots also had higher surface soil porosity, which was associated with very high infiltration rates.
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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 | 0.000 | 0.000 |
| 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".