Acknowledgment to Reviewers, 2022
Bibliographic record
Abstract
The Asian Pacific Journal of Tropical Biomedicine would like to thank all reviewers for their expertise and sparing their precious time to review articles. Their efforts have contributed greatly to the continuous growth of the Journal. Here we acknowledge, with special thanks, to those who reviewed one or more papers for the Journal in 2022. All the reviewers are listed below by countries or regions: A Idoko, Nigeria A Teo, Australia Abdel Samad EL Shamy, Egypt Akbar Anaeigoudari, Iran Ali Ganji, Iran Antonella Canini, Italy Arulazhagan Pugazhendi, Saudi Arabia Arunkumar Elumalai, India B Abdallah, Denmark C Seonheui, Korea D Arya, India D Famakinde, Nigeria E Elfayoumy, Egypt E Gajda, Poland Elham Ahmadian, Iran Esraa Ahmed, Egypt G Fragoso-González, Mexico G Rigane, Saudi Arabia Gautam Sethi, Singapore Haleh Vaez, Iran Hamid Tebyaniyan, Iran Huseyin Erol, Turkey I Abubakar, Uganda K Ho, Korea Khurshid Jalal, Pakistan K Maithal, India L Beber, Brazil L Liu, China M Ashrafizadeh, Turkey M Basaran, Turkey Mahmoud Khalil, Egypt Maria Adelina Jiménez-Arellanes, Mexico Muhammad Furqan Akhtar, Pakistan Muhammad Qasim, Pakistan Olga Wesołowska, Poland Omyma Ahmedn Abou Zaid, Egypt P Deo, Australia Jae Ho Park, Korea P Pocasap, Thailand R Khan, Pakistan R Sistla, India Radhiga Thangaiyan, India Ramprasath Vanu Ramkumar, Canada S Abbes, Tunisia S Davaran, Iran S Gao, United States Salar Hafez Ghoran, Iran Sankarganesh Arunachalam, India Seyed Alireza Esmaeili, Iran Sundaresan Arjunan, India T Srisongkram, Thailand Talha Bin Emran, Bangladesh V George, India V Madic, Serbia VijayAnand M, Korea Virginia Concato, Brazil W Chang, China W Vongsangnak, Thailand Y Bao, United Kingdom Y Chiu, China Yeliz Demir, Turkey Z Kozovska, Slovakia
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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 | 0.019 | 0.186 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.121 | 0.119 |
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".