Bibliographic record
Abstract
It is our great pleasure to welcome all of you to 2020 7th International Conference on Coastal and Ocean Engineering (ICCOE 2020) April 22-24, 2020, Singapore. ICCOE 2020 is dedicated to issues related to Coastal and Ocean Engineering. Due to the COVID-19 pandemic, the conference committees made a decision to hold an online conference instead to ensure every participant’s personal safety. CCOE 2020 is highlighted with four keynote speakers: Prof. Gordon Huang (University of Regina, Canada); Prof. Chou Loke Ming (National University of Singapore, Singapore); Prof. Koh Hock Lye (Sunway University, Malaysia); Associate Professor Edmond Yat-Man Lo (Nanyang Technological University, Singapore). The Conference had one poster session and four oral sub-sessions on different topics: Environmental Science and Engineering, Innovation and Economic Development, Ocean Engineering, Marine Science and Ship Engineering. It was a great opportunity for students, researchers and engineers to interact with the experts and specialists to get advice or consultation on technical matters, dissemination and marketing strategies. This volume of the proceedings presents a selection of papers submitted to the conference. They are organised in nine chapters under the topics of: Utilization of Ocean Space, Marine Resource Exploitation, Utilization of Ocean Energy, Ship Engineering, Marine Dynamics, Architectural Design, Fire Safety Engineering, Environmental Science and Technology, and Economic Innovation. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on high quality and relevance to the conference theme. We express our sincere appreciation to the organizing committee and the volunteers who had dedicated their time, effort and help in planning, promoting, and organizing the conference. Prof. Chou Loke Ming National University of Singapore, Singapore 2020-04-29
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.508 | 0.357 |
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".