Rendement énergétique des navires et bruit rayonné sous l’eau
Bibliographic record
Abstract
The report presents the results of a review of means for technical and operational measures which can: a) increase the Energy Efficiency and reduce Greenhouse Gas emissions from ships, and/or b) mitigate underwater radiated noise (URN) from ships, which can have damaging effects on marine animals of many types. In some cases, measures may have desirable outcomes for both aspects, while in others they may conflict. The report highlights when each of these may apply. The report text introduces some of the key issues related to Energy Efficiency, Greenhouse Gas and URN, and to the current initiatives at the International Maritime Organization (IMO) which relate to these. Measures are consolidated in the matrix that provides an overview of each measure, and a summary of its effectiveness for Energy Efficiency, Greenhouse Gas and URN. Other aspects of each measure are also outlined, to define the advantages and benefits to the ship’s design and operations; disadvantages and challenges; technology readiness level; cost impacts for implementation and operation; and applicability to different ship types. A wide range of mitigation measures are available. All will incur some level of cost, but in most cases there are co-benefits between Energy Efficiency and URN that may offset some or all of this disadvantage. Main recommendations are: There is a need for increased data collection and knowledge dissemination. For both Energy Efficiency/Greenhouse Gas and URN there is a lack of high-quality, measured data on the effectiveness of many of the methods that have been proposed. This increases risk and uncertainty for owners who wish to improve the performance of their ships, and will delay any improvement of the overall global fleet. IMO, and its member administrations, should take and encourage steps to improve this situation.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".