Fracking as a guarantee of Energy Security in countries with low conventional oil reserves
Bibliographic record
Abstract
Introduction— Fracking is one of the most recent techniques presented by the hydrocarbon industry to guarantee the extraction of oil from non-conventional fields and thus maintain the energy security of many countries such as the United States, China, Canada, and even is one of the potential practices in Colombia. Although it is a promising technique because of its advanced level of engineering, its impacts, and environmental effects are today a subject of debate partly due to limited information and limited detailed research on the consequences. Objective— This article presents the generalities of the technique, a historical timeline, advantages and disadvantages of the implementation, successful cases, and the implications on the environment. Methodology— This is achieved through a review and classification of information from studies and research developed in the world on the named technique. Results— The advantages are clear: increase in proven reserves, energy independence, increased employment, economic development, among others. Conclusions— There are disadvantages such as excessive use of water, use of toxic additives, generation of earthquakes, and negative environmental effects. All of the above is related to previous investigations carried out with fracking.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".