Acknowledgement to Reviewers of Maritime Technology and Research in 2022
Bibliographic record
Abstract
The editorial team greatly appreciates the reviewers who have dedicated their considerable time and expertise to the journal’s rigorous editorial process in 2022, regardless of whether the submissions were finally published or not. In 2022, a total of 59 articles were submitted to the journal, with a median time to first decision of 78 days, and 108 days from submission to publication. The editorial team would like to express their sincere gratitude to the following reviewers for their generous contribution in 2022: Abdullah Açık, Turkey Adam Prokopowicz, United States Agnieszka Lazarowska, Poland Aiya Chantarasiri, Thailand Alexis Papathanassis, Germany Amnuay Kleebayoon, Cambodia Anas Alamoush, Sweden Anastasia Christodoulou, Sweden Anita Gudelj, Croatia Antreas Kantaros, Greece Baharak Ashrafi, Germany Baki B. Sadi, Canada Behrang Beiranvand, Iran Bihong Lv, China Boyan Mednikarov, Bulgaria Carl-Uwe Böttner, Germany Chalermpong Senarak, Thailand Chandrashekher Umanath Rivonker, India Ching-Chiao Yang, Taiwan Chutarat Noosuwan, Thailand Corina Varsami, Romania Dang Duc Nhan, Vietnam Dominique Lepore, Italy E. M. Smith Johnson, Jamaica Efe Akyurek, Turkey Effi Helmy Ariffin, Malaysia Emma Ballad, Philippines Ergul Mustafa, Turkey Francisco García Sánchez, Spain Hao Rong, Portugal Hilde Elise Heldal, Norway Hong Oanh Nguyen, Australia Hua Li, China Işık Filizok, Turkey Jagan Jeevan, Malaysia Jianmin Li, China Juan Adolfo Chica-Ruiz, Spain Juan Carlos Astudillo, Hong Kong Jun-Woo Jeon, South Korea Kachai Tam, Canada Kanwar Muhammad Javed Iqbal, Pakistan Keyuan Zou, China Kimberly Tam, United Kingdom Krzysztof Wróbel, Poland Lai Fatt Chuah, Malaysia Maciej Gucma Poland Mahmood Shafiee, United Kingdom Maneerat Kanrak, Thailand Manickam Venkataraman, India Marco Túlio Mendonça Diniz, Brazil María Araceli Losey-Leon, Spain Massimo Musio-Sale, Italy Meena Madhavan, Thailand Mei-Wo Yii, Malaysia Michael A. Tamor, United States Mihail Diakomihalis, Greece Mir Irfan Ul Haq, India Mohan Feroz Khan, India Muhammad Zainuddin Lubis, Indonesia Neil J.Douglas, New Zealand Nelly Sedova, Russia Nitin Agarwala, India Noel Hidalgo Tan, Thailand Nurkhodzha Akbulaev, Azerbaijan Olabisi Michael Olapoju, Nigeria Olaf Chresten Jensen, Denmark Paraskevi Mpeza, Greece Pelin Bolat, Turkey Peter Ralph Galicia, Philippines Phansak Iamraksa, Thailand Phindile Tiyiselani Zanele Sabela-Rikhotso, South Africa Pisit Poolprasert, Thailand Prachakon Kaewkhiaw, Thailand Punam Thakur, United States Ranil Kularatne, Sri Lanka Razon Chandra Saha, Bangladesh Ronghui Li, China Rym Ennouri, Tunisia Sachinandan Dutta, Oman Salman Nazir, Norway Saravut Jaritngam, Thailand Scott A. Edwards, United Kingdom Sim Sai Tin, China Sinlapachai Senarat, Thailand Stephen Cahoon, Australia Subha M., India Subramaniam Neelamani, Kuwait Supawat Chaikasem, Thailand Suresh Bhardwaj, India Suvaluck Satumanatpan, Thailand Theophilus Chinonyerem Nwokedi, Nigeria Victor Bolblot, United Kingdom Violeta Hansen, Denmark Volker Bertram, Germany Wenbing Zhang, China Yi-Che Shih, Taiwan Zec Damir, Croatia Zhongzhen Yang, China Zorica Đurović, Serbia
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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 | 0.034 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.019 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.029 |
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".