Likelihood of using phytoremediation for mine-tailing management in Chile
Bibliographic record
Abstract
Chile has more than 750 mine tailings across the country, mainly distributed in the northern region, which also includes a biodiversity hotspot and the driest desert in the world. So far, tailing management has included chemical and physical stabilization of tailings, exclusively. This research examined the perceived likelihood of stakeholders, namely: Academia, Industry, environmental Non-Governmental Organizations, and Government officials, in the management of tailings and explored their attitudes toward the inclusion of plants and their associated microbes, as an additional stabilization technology, through an online questionnaire (n = 43). We aimed to answer What was the perceived likelihood of stakeholders for using phytoremediation for mine-tailing management in Chile? The consensus opinion across sectors is that there is general lack of proper tailings management in Chile. There is a critical willingness to incorporate phytoremediation, with a preference for using native plants. An absence of demonstrably successful phytoremediation projects, funding and access to suitable plants are perceived to be significant limitations. Local community involvement is considered key to successful restoration of tailings.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".