2020 IMO Sulphur Regulation: Impacts and Solutions for Fednav Limited
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work is focusing on the 2020 IMO sulphur regulation and its impact on the shipping industry and more particularly Fednav’s activity. The thesis report presents a holistic approach starting with IMO emission regulations including current and future. The thesis is then moving on to Fednav business characteristic followed by the possible solutions to successfully implement the 2020 IMO sulphur regulation and stay competitive. These solutions are shortlisted to LNG and scrubber as they are the only mature technologies already implemented in the industry. They are then analyzed using both a private and welfare cost & benefit analysis to evaluate LNF and scrubber systems economic feasibility from a business and society perspective. LNG is discarded based on low bunker availability worldwide in combination with Fednav’s fleet tramping sailing patterns. For the scrubber, the opened loop solution is selected as the one offering the best return on invest- ment. After the welfare cost & benefit analysis, all scrubber solutions are also discarded because of the low pH washwater dump in the ocean. The strategy for Fednav is to make sure Canada is strict on sulphur regulation, mitigate bunker sensibility risk as well as bunker quality risk.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it