Application the Results of Energy Efficiency Audit Planning and Implementation of Fishing Vessels Modernisation
Bibliographic record
Abstract
Abstract The Energy Efficiency Design Index (EEDI) became mandatory for new ships as well as the Ship Energy Efficiency Management Plan (SEEMP) for all ships due to the MEPC 62 (July 2011) along with the adoption of amendments to MARPOL Annex VI by the Parties to the MARPOL Annex VI. This was the first legally binding climate change treaty to be adopted since the Kyoto Protocol. The Ship Energy Efficiency Management Plan (SEEMP), which has been obligatory since 2013, did not to cover fishing cutters but triggered the implementation of energy efficiency audits due to fishing limits, high fuel prices, and free freight market. A research team of the Maritime University of Szczecin conducted energy efficiency audits on several fishing vessels operated on the Baltic Sea and provided the results to the shipowners. On their grounds, the shipowners started seeking methods to reduce energy consumption. They began modernizing the vessels using the information collected in the reports on energy audits. Usually, the modernization process is performed in several stages. Apart from energy recovery installations, fishing vessels were furnished with the latest technologies aimed at the reduction of energy consumption and at making ships greener. The paper presents results of the said process at the selected fishing cutters operating on the Baltic Sea. The paper includes also the recommendations on the directions of the following stages of the audits.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".