Bibliographic record
Abstract
Over the past few years, as maritime trade and traffic were highly expanding, problem of invasive species via ballast water have been raised. In 1988, Canada and Australia had firstly experience that unexpected and hazardous species were observed on their own sea, they have issued the problem to MEPC under the IMO. At the end of many years of discussion, on the diplomatic conference in 13 Feb. 2004, International Convention for the Control and Management of Ballast Water and Sediments of the Ship was adopted. Requirements for entering into force of this Convention is that 30 countries ratify and world merchant marine fleet is more than 35% and BWM Convention will be effected after 12months from date satisfying conditions. With Finland ratifying the BWM Convention on 8 Sep. 2016, the fleet amounted to 35.1441% and ratification country became 52 countries. Therefore, after 12month, BWM Convention will be formally effected on 8 Sep. 2017. Ballast Water Treatment System is to be fitted in new ships as well as existing ships. Thus, there are concerns of ship owners to be suitably installed a variety typed BWTS in many kinds of vessels. As approaching for resolving these problems, engineering analysis was carried out research studies and detailed design to analyze to optimal installation space for retrofitting a BWTS using 3D Scanning method, targeting representative DWT 180K Bulk carrier of dry cargo vessels charged more 40% on worldwide vessel and mainly two type BWTS as electrolysis treatment type and ultra violet treatment type. Optimal design of 3D Scanning technology was applied to analyze four step process and the overall conclusion was described in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".