Siting a centralised processing centre for artisanal and small-scale mining - a spatial multi-criteria approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Artisanal and small-scale mining is one of the global phenomena that is a threat to environmental health and safety. There are ambiguities in the manner in which an ore-processing facility operates. These ambiguities can cause environmental problems and hinder the mining capacity of these miners in Ghana. The vast majority of attempts to address these problems through the establishments of centralised processing centres have failed, with only a handful of successes. This research sought to use an established data-driven, geographic information based system to locate a centralised gold processing facility within the Wassa Amenfi-Prestea Mining Area in the Western region of Ghana. The study was designed to first determine the relevant factors that should be considered in the decision-making process for locating a centralised ore-processing facility. Secondly, it sought to implement the identified factors in a case study by identifying specific geospatial techniques that can best be applied to identify potential sites. By adopting in-depth consultations with four stakeholder groups for data collection and content analysis for data analysis, thirteen relevant factors were identified. However, in the case study, due to data unavailability, only seven of the factors were considered. Geoprocessing techniques including buffering and overlay analysis and multi-criteria decision analysis were employed to develop a model to identify the most preferred locations to site a centralised processing facility. Site characterisations and environmental considerations, incorporating identified constraints, to determine an appropriate location were selected. The final map output indicates estimated potential sites identified for the establishment of a centralised processing centre. The results obtained provide areas suitable for consideration.
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.
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 | 0.001 | 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 it