Energy Saving Energetic Systems for Coastal Fishing Cutters
Bibliographic record
Abstract
Abstract The maritime environment protection is increasingly reflected in legal regulations regarding, inter alia, the harmful exhaust gas components emitted by marine combustion engines. The provisions imposing the emission limits for SOx, NOx and CO2 are included in MARPOL 73/78 ANNEX VI adopted by the International Maritime Organization (IMO). However, as of today, these provisions are not applicable to fishing cutters. One of the methods, both to decrease emissions’ volume and also to reduce the operating costs of ships, is to lower fuel consumption of marine energetic systems. The paper presents a proposition of energy-efficient and environmentally friendly energetic systems for coastal fishing cutters. It also demonstrates the importance of the said systems and includes the elaboration regarding the impact of fuel type, renewable energy sources and energy conversion methods on the hazards to the environment caused by the emission of harmful exhaust gas components. The presented solutions refer to fishing cutters of the length of 15-30 m and are categorized into two groups. The division criterion applied is an access to technologies currently available and future technologies enabling the use of alternative energy sources.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".