Bibliometric Analysis of Earthquake Research in America: A Comparative Study Using Web of Science and Scopus Databases
Bibliographic record
Abstract
In the American continent, the movement of tectonic plates causes seismic activity that generates earthquakes.Some countries of America are located on the western fringe of the Pacific Ring of Fire, which involves several tectonic plates, such as Nazca, Cocos, and North American, among others.This study aims to analyse the scientific production of Earthquakes in America (EiA) using Web of Science and Scopus databases, to understand the origin (first publications in the area), intellectual structure (bibliometric maps), and research trends (topics for future works).The methodology was based on: (i) criteria for database selection (reasons for selecting Scopus and Web of Science), (ii) data processing (merging databases, deleting duplicates or erroneous documents, and the selection of bibliometric software), and (iii) analysis of intellectual structure (performance of scientific publications using bibliometric maps).The EiA has greatly impacted academia since its first publication in 1861, analysing 6553 scientific publications from the western part of the Pacific Ring of Fire.It includes the most representative subduction zones in the world, with major records in the last nine years, having contributions mainly from Mexico, France, Guatemala, and Chile.Finally, the trend topics aligned to EiA are ground motion models, seismic hazards, and induced seismicity.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.169 | 0.278 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".