The effect of cover system depth on native plant water relations in semi-arid Western Australia
Bibliographic record
Abstract
Cover systems utilising the store and release concept, i.e. evapotranspiration (ET) covers, are reliant on plant transpiration and evaporation to preclude percolation (deep drainage) into waste rock, thus minimising the risk of releasing potentially contaminated seepage. However, attaining persistent plant communities on ET covers is especially challenging in water limited environments. Soil texture permitting, greater water storage may be achieved through increased cover thickness. This study quantified cover material water dynamics, growth, and water use of native Australian plants over one year to determine if differences in plant performance were associated with species, plant available water, and cover thickness on a 1.5 year old irrigated ET cover in a semi-arid region. Plant height growth varied between species but not with cover thickness. Average transpiration per unit leaf area and stomatal conductance (gs) were 1.2 and 2.3 times higher in winter than in summer, respectively, and tended to be higher on thicker covers for both seasons. Overall, transpiration rates were positively correlated with soil volumetric water content (VWC, average from 0.0–0.3 m), but differed between species. Transpiration tended to increase with VWC, gs, and cover thickness (0.7 > 0.5 > 0.3 m), indicating plant (stomatal) control of transpiration in response to drought stress associated with cover thickness. The analysis suggests that plants on thicker covers transpired at greater rates due to access to stored water at greater depths, resulting in higher overall transpiration. This work demonstrates the importance of quantifying water use differences between species, seasons, and cover thicknesses during cover system modelling and design phases. It also highlights the potential for greater plant available water by increasing cover thickness, aiding the establishment of self-sustaining plant communities on ET covers.
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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 | 0.000 | 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".