Carbon Sequestration: an Answer to the World’s Carbon Dioxide Emission Problem
Bibliographic record
Abstract
As of November 2007, 174 parties had ratified the Kyoto protocol signifying a large part of the solution to one of the worlds primary environmental problems; carbon dioxide emissions. Although the United States refused to sign the protocol, their neighbours in Canada were eager to address the issue and sign. However with oil being a major Canadian export, carbon dioxide emission reduction was arguably improbable and unprofitable. With the pressure of reducing carbon dioxide emissions an imminent, carbon sequestration may be the symbiotic solution in satisfying Kyoto, saving the environment and even increasing profitability. Carbon sequestration is the process where carbon dioxide is injected into an oil well in order to increase recovery. With tertiary oil recoveries driving much of the oil business, cheap and efficient recovery methods are invaluable. Presently there is a Canadian operation in Wayburn, Saskatchewan which employs the technique. In addition, Texas and Scandinavian oil companies are using Carbon dioxide injection. If carbon sequestration increases oil recovery it has to be the preferred method. By purchasing carbon dioxide from external sources and recycling their own, companies can reduce emissions while increasing profits. Finally it may be profitable to save the environment.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".