Mapping The Structure Nose Surgery Discipline In Science Citation Index (Sci) During The Period Of 1999-2012
Bibliographic record
Abstract
Aim: Scientometric studies are among the most effective methods for evaluating the status of scientific outcomes. These studies are considered as the most practical methods for evaluating the scientific production of countries, individuals and organizations. Using the Web of Science (WoS), the present study aimed to Mapping the structure Nose Surgery discipline in Science Citation Index (SCI) During the Period of 1999-2012 Method: This study is a descriptive-analytical research which was conducted using the scientometric indices. All materials produced in the field of nasal surgery, with 2282 scientific records, were reviewed on the Web of Science database. The analysis was done using the softwares of HistCite, VOSviewer, and Exel and finally, a map of science-based on the nasal surgery field was drawn. Findings and Conclusion: The United States accounted for the 870 records and 38.124% of the total documents; Turkey with 259 records and 11.350% of the total number of documents was in the second rank of the scientific production in this field. Iran was placed in the 9th rank with regard to the production of documents on the nasal surgery with 53 records and 2.322% of the total documents. Most of the materials are in the form of papers which include 1732 records (71%). The University of Texas was ranked in the first position with 96 records (4.207%). Authors from the US had the highest level of collaboration with the authors of other countries, followed by the authors from Germany, England, Australia, and Canada. Keywords: Mapping of Science, Nose Surgery, Science Citation Index (SCI)
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.034 | 0.055 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".