Recursos minerales y energéticos y su industria
Bibliographic record
Abstract
The resources of the Arctic are very varied, including both large \ndeposits of exploited oilfields and possible reserves of deposits \nnot yet discovered. On the other hand, mining also has and has \nhad a fundamental role in the history of many of the Arctic countries, as in the case of the gold rush in Alaska or the large deposits in Siberia and Canada. The discovery of new deposits in areas \nsuch as Greenland or the deep sea surely could influence the \ngeopolitical future of these countries. \nThis chapter will emphasize both the richness of present resources and the potentiality of the reserves for the future. The Arctic \nhas now become a strategic area for resource exploration. This \nis partly due to climatic changes that are causing a decrease in \nthe ice cover, allowing access to hitherto unexplored areas such \nas the ocean floor. To this it must be added the decrease in new \ndiscoveries of land resources and the gradual decline in the quality of the deposits under exploitation. All this together with the \nincrease in the demand for resources in a society in continuous \ngrowth, makes it necessary to find a balance in the geopolitical, \nsocial, and environmental framework.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".