Acknowledgement to Reviewers of Maritime Technology and Research in 2023
Bibliographic record
Abstract
The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous peer review process in 2023, regardless of whether the submissions were finally published or not. In 2023, a total of 50 articles were submitted to the journal, with the median time to first decision of 87 days, and 117 days from submission to publication. The editorial team would like to express sincere gratitude to the following reviewers for their generous contribution in 2023: Abdullah Açık, Turkey Albina Pashkevich, Sweden Alcino E. Ferreira, France Amnuay Kleebayoon, Cambodia Anastasia Christodoulou, Sweden Anish Arvind, Hebbar, Sweden Antreas Kantaros, Greece Apostolos Papanikolaou, Greece Baharak Ashrafi, Germany Bihong Lv, China Chandrashekher Umanath Rivonker, India Chatnugrob Sangsawang, Thailand Ching-Chiao Yang, Taiwan, China Chutarat Noosuwan, Thailand Corina Varsami, Romania Dang Duc Nhan, Vietnam Debabrata Karmakar, India Diego Silva, Spain Dimitrios Dalaklis, Sweden Dobrin Efremov, Bulgaria Elena Romano, Italy Emma Ballad, Philippines Enzo Pranzini, Italy Fatima Zohra Bouthir, Morocco Florin Rusca, Romania Francisco García Sánchez, Spain Gairuzazmi Mat Ghani, Malaysia Giambattista Guidi, Italy Giulio Dubbioso, Italy Grienggrai Rajchakit, Thailand Hanna Barbara Rasmussen, Denmark Helga Pavlić Skender, Croatia Hilde Elise Heldal, Norway Hong Oanh Nguyen, Australia Hua Li, China Hung Yung-Tse, United States of America I Ketut Aria Pria Utama, Indonesia Ivan Mraković, Montenegro Jagan Jeevan, Malaysia Jiqiang Li, China Joy Bhowmik, Bangladesh Juan Carlos Astudillo, Hong Kong, China Junmin Mou, China Kachai Tam, Canada Kadda Boumediene, Algeria Kantapon Tanakitkorn, Thailand Lee Shin Yun, Malaysia Li Song, China Liangfeng Li, China Lirola-Delgado Isabel, Spain Livingstone Divine Caesar, United States of America Luka Vukić, Croatia Maciej Gucma, Poland Mahinda Bandara, United Kingdom Malgorzata Wolska, Poland Manickam Venkataraman, India Marco Túlio Mendonça Diniz, Brazil Marie Antonette Juinio-Meñez, Philippines Mate J. Csorb, Norway Mesbah Saybani, Iran Mohamed Zbair, Finland Muhammad Abu JamiIn, Indonesia Muhammad Zainuddin Lubis, Indonesia Mumini Dzoga, Kenya Nagavinothini Ravichandran, Italy Neil J. Douglas, New Zealand Nitin Agarwala, India Nopparat Pochai, Thailand Nucharee Nuchkoom Smith, Thailand Omer Berkehan Inal, Turkey Orestis Schinas, Germany Oznur Oztuna Taner, Turkey Phansak Iamraksa, Thailand Porpattama Hammachukiattikul, Thailand Pritam Tripathy, India Puyang Zhang, China R. Nagalakshmi, India Raju Ahmmed, Bangladesh Razon Chandra Saha, Bangladesh Ruiyong Mao, China Sante Francesco Rende, Italy Sathit Pongduang, Thailand Scott Edwards, Japan Senthil Kumar Madasamy, India Serdar Beji, Turkey Shariful Islam Shakeel, Bangladesh Shivaji Ganesan T., India Shweta S. Kaddi, India Sim Sai Tin, China Sittichai Pimonsree, Thailand Siwarut Laikram, Thailand Sri Suharti, Indonesia Srinivasan Chandrasekaran, India Stephen Cahoon, Australia Subha M., India Sun Wei, China Supawat Chaikasem, Thailand Surasak Phoemsapthawee, Thailand Thanapong Phanthong, Thailand Thies Thiemann, United Arab Emirates Tsz Leung Yip, Hong Kong, China Velayutham Rajendran, India Victor Bolblot, Finland Viv Djanat Prasita, Indonesia Walaa Altop, Iraq Watcharapong Chumchuen, Thailand Wenresti Gallardo, Oman Xin Liu, China Xishu Li, United Kingdom Yii Mei-Wo, Malaysia Yogesh J. Chauhan, India Zurab Bezhanovi, Georgia
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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 | 0.030 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.023 |
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".