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 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 | 0.036 | 0.204 |
| 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 | 0.020 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.041 | 0.032 |
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".