Vegetating mine tailings: The benefits of using non-native species in the remediation of a bauxite residue site in Jamaica
Bibliographic record
Abstract
The impact of non-native plant species, particularly invasive species, on biodiversity has been investigated and documented for decades and the general consensus is that they can pose risks and have a negative effect on native flora and fauna. However, there can be ecological and conservational benefits from using non-native plant species in the mine closure process, where remediation of process waste within a defined time period is often a requirement and presents greater challenges compared to a normal mine site. Initial remediation work on Rio Tinto’s bauxite residue sites in Jamaica commenced in the mid-2000s, and with the majority of the vegetation work now completed this paper aims to present some of the advantages of using non-native species in the remediation programme undertaken at a bauxite residue disposal site. In addition, the paper discusses the use of non-native plants in mine closure projects generally, local challenges surrounding the awareness and management of species that are now considered invasive, and some direction when local legislation is absent. The aim of this paper is to offer another perspective to the general view of negative impact of non-native species and demonstrate how some non-native, and even invasive plants, could play a beneficial role in certain cases in the field of tailings site closure.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".