Bibliographic record
Abstract
Afghanistan is a geological complex in which plentiful minerals and fuels were formed. During the last 100 years, geologists from Great Britain, France, Germany, Italy, Japan, Canada and the United States explored that country and produced many excellent reports and maps. Real progress in a systematic analysis of Afghanistan&s;s natural resources, however, was not made until the intensive efforts of the U.S.S.R. in the past two decades. By diplomatic and economic maneuvers, the Soviets took control of Afghanistan&s;s nascent hydrocarbon industry during the 1960s. After the Daoud coup of 1973, the Russians and their Afghan supporters replaced pro-Western technical advisors, made all geological reports and maps state secrets, and hampered Western-linked development projects. By 1977, intensive field investigations by numerous Russian geologists led to the discovery of hundreds of mineral deposits, occurrences, or shows---as well as several excellent petroleum prospects. Many have claimed that Afghanistan is poor in natural resources; this conclusion is incorrect. Total Russian aid to that country since 1955 has been $1.3 billion, and in 1979, some $652 million was committed to mineral-resource exploration and development. The present Russian military occupation of Afghanistan is partially subsidized with Afghanistan resources. Resource acquisition by the U.S.S.R. in Afghanistan is thus a most important factor in the world mineral situation today.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".