Atmospheric, Water and Acoustic Pollution from Hydrocarbon Activities in the American Continent: A Literature Review
Bibliographic record
Abstract
Hydrocarbon activities over the years have been one of the main sources of environmental pollution, creating short and long-term impacts. This study aims to analyze the scientific production of the American continent through a bibliographic review of scientific articles published from the 1970s to the present, in order to contrast relevant scientific information about the types of pollution, water, atmospheric, and acoustic, published in the most important scientific repositories in the world, such as Scopus and Web of Science. The Prisma methodology was adopted for its development. From the plethora of articles collected, a sample of 3879 scientific articles was extracted, from which 3322 of them were excluded, leaving 557 records with remarkable information such as: country, year of publication, type of contamination, remediation if applicable, the associated oil & gas sector, and publication registration on the indexed website. It was noted that the countries with the highest scientific production are the United States, Canada, Brazil, Mexico, and Argentina. Furthermore, the Web of Science, unlike Scopus, contains more indexed publications related to the types of contamination objects relevant to this study. On the other hand, publications focused on water pollution are the only ones that come up with remediations; the rest release a smaller number of publications on these topics.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".