Coal Industry of the USA: Main Problems and Ways of Solution
Bibliographic record
Abstract
Coal Industry of the USA: Main Problems and Ways of SolutionThe United States of America has a large production power and huge development potential.In the early postwar decades, the US leading position in the world economy was indisputable.The war saved the country from serious competitors, but not for a long period of time.However, this situation has significantly changed due to the economic recovery taken place in Western Europe and industrial jerk of Japan.The fall in the U.S. overall level of mining and, in particular, gold production, can be explained not only by objective reasons, for example, the depletion of the producing fields, but by a number of challenges involed into the mining policy of the country as well.One of these problems is a long procedure required to obtain necessary permission and mining license for carrying out mining activities.This aspect is considered to be a major barrier preventing from increasing investments and enhancing production capacity in the United States.But the most important problem is related with production costs.In comparison with other countries the costs of developing mining fields in the United States are very high.The reason is that working out mineral resources of developing countries where production costs are much lower than in the US, Canada and Australia is more profitable business for large international corporations.As a result, imports increase in the share of extracting mineral raw materials and fuel, while the role of the national mineral base is reduced.In modern conditions of globalization of world economy improving mineral resource base involves transnational cooperation in prospecting and exploration of new deposits in the most promising regions of the world, the international division of labor for the extraction and processing of mineral raw materials using advanced technology.It should be emphasized that while implementing the most advanced technologies such issues as economic, social and political interests of individual countries are taken into consideration.Much attention is paid to strengthening mutually beneficial trade relations between countries in terms of supplying scare mineral raw materials and processing products and introducing the latest world achievements in mining and geological practice.A variety of natural conditions and resources still remains a good natural base for economy development.However, the scale and wasteful patterns of application worsen the conditions of the natural environment and, at the same time, lead to increasing air and water pollution.That is why we need to move on to different strategies of using natural resources to provide safe and healthy style of work and enhancing current approaches applied for developing natural resources will help preserve mineral wealth for future generations.
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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 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".