Acknowledgement to Reviewers of Maritime Technology and Research in 2020
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 2020, regardless of whether the submissions were finally published or not. In 2020, a total of 45 articles were submitted to the journal, with a median time to first decision of 50 days, and 71 days from submission to publication. The editorial team would like to express their sincere gratitude to the following reviewers for their generous contribution in 2020:
 Agnieszka Lazarowska, Poland
 Akihiko Matsuda, Japan
 Aldo Chircop, Canada
 Anand Kumar, Malaysia
 Anastasia Christodoulou, Sweden
 Anthony Paul Sison Guerrero, United States
 Anthony Yaw Karikari, Ghana
 Arunachalam Ponshanmugakumar, India
 Asen Asenov, Bulgaria
 Birgit Pauksztat, Sweden
 Boris Svilicic, Croatia
 Carlos Efrén Mora Luis, Spain
 Chalermpong Senarak, Thailand
 Chandrashekher Umanath Rivonker, India
 Che Abd Rahim Mohamed, Malaysia
 Christiaan Adika Adenya, Kenya
 Christopher Nolan, United States
 Dimitrios Dalaklis, Sweden
 Dong-Taur Su, Taiwan
 Ergun Demirel, Turkey
 Fatima Zohra Bouthir, Morocco
 Florin Rusca, Romania
 Floris Goerlandt, Canada
 Fu Ming Tzu, Taiwan
 Geng-Ruei Chang, Taiwan
 George H. Kaplan, United States
 Giulio Dubbioso, Italy
 Hao Long, China
 Hong Oanh Owen Nguyen, Australia
 Jacopo Aguzzi, Spain
 Jagan Jeevan, Malaysia
 Jerónimo Esteve-Perez, Spain
 Jiangang Jin, China
 Jianjun Wu, China
 Jianmin Li, China
 Jiao Jialong, China
 Juan Carlos Astudillo, Hong Kong
 Jun Ando, Japan
 Kantapon Tanakitkorn, Thailand
 Kwan Ouyang Taiwan
 Laura Piñeiro, Spain
 Li Ye, China
 Lidong Fan, Australia
 M. P. R Prasad, India
 Maciej Reichel, Poland
 Mahinda Bandara, Sri Lanka
 María-Araceli Losey-Leon, Spain
 Marta Mańkowska, Poland
 Maruj Limpawattana, Thailand
 Masayoshi Doi, Japan
 Mate J. Csorb, Norway
 Ming-Cheng Tsou, Taiwan
 Mohammed Russtam Suhrab Ismail, Malaysia
 Mohd Hazmi Bin Mohd Rusli, Malaysia
 Moses Kopong Tokan, Indonesia
 Mumini Dzoga, Kenya
 Neil J. Douglas, New Zealand
 Nucharee Nuchkoom Smith, Thailand
 Oghenetejiri Digun-Aweto, South Africa
 Olabisi Michael Olapoju, Nigeria
 Olaf Chresten Jensen, Denmark
 Om Prakash Sha, India
 Paul Tae-Woo Lee, China
 Pengfei Zhang, United Kindom
 Peter RANERI, Sweden
 Peter Ralph Galicia, Philippines
 Phansak Iamraksa, Thailand
 Phatchara Sriphrabu, Thailand
 Proshanto Mukherjee, China
 Saikat Banerjee, India
 Sarinya Sanitwong Na Ayutthaya, Thailand
 Seonho Cho, Korea
 Sheree-Ann Adams, Grenada
 Supawat Chaikasem, Thailand
 Surasak Phoemsapthawee, Thailand
 Suresh Bhardwaj, India
 Thee Chowwanonthapunya, Thailand
 Vasilios D. Tsoukalas, Greece
 Wirachaya Chanpuypetch, Thailand
 Yodchai Tiaple, Thailand
 Yottana Khunatorn, Thailand
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".