A brief report on Iranian Journal of Basic Medical Sciences in 2013.
Bibliographic record
Abstract
This editorial deals with the state of the Iranian Journal of Basic Medical Sciences (IJBMS) in 2013. In the previous year, we received 613 manuscripts for publication. After peer-review by expert national and international reviewers, 15 % of submitted papers were accepted for publication. As presented in Figure 1, the rejection rate was around 60%. Figure 1 Analysis of manuscripts received in 2013 by Iranian Journal of Basic Medical Sciences The publication frequency was doubled by switching from bimonthly to monthly, with around 12 papers per issue. Most of the published papers were original articles; review articles were around 5% of all published papers (Figure 2). Figure 2 Types of manuscripts published in 2013 by Iranian Journal of Basic Medical Sciences Contributions of the international authors were increased compared to the previous years (Figure 3). In 2013, we published papers by corresponding authors from various countries including China, India, Saudi Arabia, Malaysia, Pakistan, Serbia, and Turkey. Contributing authors were from Austria, Canada, Egypt, Lebanon, The Netherlands, Nigeria, and The United Kingdom. Figure 3 International collaboration in published manuscripts in Iranian Journal of Basic Medical Sciences Two special issues were published in 2013. The first one focused on Saffron (Crocus sativus). As Saffron is one the most famous plants cultivated in Iran and has various pharmacological effects including anti-asthmatic, anti-carcinogenic, anti-mutagenic, anti-depressant, and immunomodulating and has antioxidant-like properties, it was selected for a special issue. The second issue was conducted with 20 papers on Human T-cell lymphotropic virus-I (HTLV-I), as this virus is endemic in several regions of the Middle East including Iran; various concepts about this virus and related diseases were discussed. Finally, I should thank all authors and reviewers who contributed to the IJBMS publication process. I hope to publish this journal with better papers and higher quality in 2014. According to the citation analysis done recently, a better impact factor (IF) is expected for the current year.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.015 |
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".