Bibliographic record
Abstract
To reduce damage to the environment, both locally and globally, while meeting the rising demand for oil is a big challenge for oil companies. This presentation explores what has been done to protect the environment in Sakhalin II and Canadian oil sands projects, touching on the complexity of environmental issues and important roles which oil companies should play.Of the many environmental issues faced today, the risk of global climate change is now growing and generates enormous public interest. In the meantime, oil is projected to maintain a major position in supplying primary energy for a long time to come. To tackle this problem, increases in energy efficiency and a shift to non-fossil fuels are of utmost importance.Along with these measures, gas flaring reduction and carbon capture and storage (CCS) are also very effective ways to reduce global warming gas emissions.In spite of the efforts taken by individual oil companies, the amount of global gas flaring still remains at a high level because of several constraints encountered in flaring countries. While CCS is a very promising option for drastically reducing emissions if applied at large power plants which burn fossil fuels, there is still a long way to go for this option to be accepted as a reliable and affordable means.This presentation reviews international efforts to promote these two measures and provides information on how they are progressing in Russia, Nigeria, Canada and the EU.Lastly, the presentation highlights some specific topics in related international carbon reduction efforts including CDM, CCS EOR, emissions trading and increasing public awareness in environmental issues.The concluding message is that the challenge for sustainable energy development in the new century has already started, and the roles of oil companies are essential as a main player in the high energy society.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.153 | 0.058 |
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".