بررسی بروندادهای علمی مهندسی ایران در نمایه استنادی علوم قابل دسترس از طریق پایگاه اطلاعاتی دایالوگ طی سالهای 1990 تا 2008
Bibliographic record
Abstract
This research intends to analyze Iranian Engineering Scientific Outputs during 1990-2008 in the Dialog Database. Dialog is a collection of more than 900 databases that covers also the ISI Science Citation index as SciSearch since 1990. This research has been done by scintometrics method and citation analysis. The number of documents indexed by Iranian writers in Science Citation Index has been 8396. Growth of Scientific outputs in the field of engineering during this period is 24 percent. Kaveh is the most productive author with 82 documents and ASTM is the highest cited author with 120 citations. The highest volume of documents indexed in the dialog related to Chemical Engineering and Electrical Engineering subject categories respectively. Iranian Journal of Chemistry & Chemical Engineering with 348 documents has released the highest number of productions. Among universities and research institutes, Sharif University of Technology has the highest scientific outputs in this area. Articles have the highest percentage of documents published in ISI journal by 97/24 percent and English with 99/8 percent is the main language of the published documents. The highest rate of participation of Iranian writers for publishing documents is with U. S. and Canada respectively.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".