Rehabilitation and Landscape Integration of a Tailings Deposit in Atacama, Chile
Bibliographic record
Abstract
Phytotechnologies are technologies based on the use of plants for improving environmental problems in order to detect, degrade, remove or contain contaminants in soil, groundwater, surface water, sediments or air.The use of phytotechnologies on tailings deposits is an innovative solution to mitigate the emission of particulate material, minimize wind erosion, and to improve environmental conditions and safety.Native and endemic plants are recommended, since are acclimatized to the local environment and favour natural ecological successions.The Huasco Pellets Plant (HPP), located 5 km southwest of the city of Huasco and 700 km to the north of Santiago de Chile, produces agglomerates of iron minerals.In compliance with the requirements of the Environmental Authority, the company presented a Filtered Tailings Deposit (FTD) project with a storage capacity of 14.6 million tonnes of tailings (7.6 million m 3 ).The project includes the coverage of the tailings with granular material, soil and vegetation during the progressive closure to integrate the FTD into the landscape, once the operation ceases.To guarantee a successful closure of the FTD, a phytotechnological program developed with the purposes of 1) selecting plant species for the progressive closure; 2) designing and supervising the installation and operation of a plant nursery on site; 3) designing, supervising and monitoring an experimental pilot of the phytotechnological program and 4) the elaboration of a methodological guide.To the date, the phytotechnological program has achieved the selection of the native plant species Frankenia chilensis, Jarava plumosa, Nolana sedifolia and Nolana divaricata, the implementation and operation of the nursery, and the tolerance to tailings sands of 2 of the species, together with the identification of zones differentially affected by wind erosion.The main challenges for the execution of the phytotechnological program are the governance of the project, the inclusion of the FTD closure plan from the beginning of the operation, the effective communication with the community, the companys experience in R&D projects, regulations and guidelines for the rehabilitation of mining sites and vulnerability of biological systems to climate change.We recommend that any phytotechnological program for the rehabilitation and landscape integration of a mining tailings deposit must address these challenges, in order to minimize the risk of technological implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".