Effectiveness of deep sea tailings as a waste management process in mines in Papua New Guinea
Bibliographic record
Abstract
The Mining industry is very important for the economy of a country as it provides revenue needed by the country. The manner of operation of the mines creates other economic benefits such as employment, education, health and infrastructure vital for the people. However with these benefits come environmental concerns regarding waste disposal. Mining in developing nations is controversial due to the impacts of the mines on cultural, physical and socio-economic aspects of the local communities (McKinnon 2002). Lihir and Ramu mines in Papua New Guinea are using DSTP to dispose of the waste from the mine. The former Misima mine also used this disposal method. The mining companies are responsible for the waste management and monitoring as the PNG government does not have a policy for DSTP (Koma 2001). This is because there is no specific law in PNG that deals with management and monitoring of mine tailings (Mine Watch Canada 2009). This makes it difficult for any independent body and then for the government to get a second opinion on the impact of mining. There is an increasing pressure on the mining companies to clean up the toxic mess they have produced (McKinnon 2002). Misima and Lihir mines are examples of mining operations that have environmental impacts, both socio-economic and physical. This paper will discuss the problems of deep sea tailings placement at these mines (Lihir and Misima). Ramu mine is in its early stage and therefore will not be discussed. It will also look at the general background and locations of other mines in the Asia Pacific Region that use the DSTP method of waste disposal and the policy instruments in place in these countries, with the addition of one First World mine, a Canadian one, to compare how the issue of DSTP is treated in a developed nation. Then it will review the policy instruments used in the PNG mines, its weaknesses, and the possibility of using other policy instruments to improve the environmental performance of the mines.
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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 | 0.001 | 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 | 0.001 |
| 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".