Can graminoids used for mine tailings revegetation improve substrate structure?
Bibliographic record
Abstract
The seeding of agronomic graminoid species that are tolerant to the compacted and low aeration conditions associated with mine tailings allows for rapid cover of mine waste, which in turn controls erosion. These graminoids can be used as primer-species on mine tailings to improve the rooting of other plant species, which may not tolerate soil compaction and low aeration. Tailings colonization by graminoid roots could improve ecological filters such as low air-filled porosity and elevated bulk density. The effect of above- and below-ground development of graminoid species used for hay-field seeding on the macroporosity and density of gold mine tailings was studied under controlled conditions as well as in situ. All of the graminoid species tested improved the macroporosity of the tailings after only 2 months of growth under greenhouse conditions, but had no effect on the density of the tailings. The perennial Bromus inermis Leyss. was most efficient in improving the macroporosity of tailings, having greater root diameter, biomass, and volume. The annual Avena sativa L. also produced high root biomass and length, which improved the macroporosity of the tailings. However, under field conditions, graminoids had low cover and no effect on macroporosity, which highlights that their growth should be improved to make them usefull as primer-plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".