Bottom-Up Approach Ship Emission Inventory in Port of Incheon Based on VTS Data
Bibliographic record
Abstract
As a result of the rapid growth of international trade, atmospheric pollution from transportation has been more topical than ever, especially in dense hub port-cities. The shipping industry should pay more attention corresponding to its contribution to local atmospheric pollution. This paper supports the application of data collected from the vessel tracking service system with a bottom-up approach to generate a comprehensive 2019 local ship emission inventory at Port of Incheon. The calculated emission inventory presented the dominance of CO2 emission and the considerable contribution of NOx and SOx emissions, the significant contribution of auxiliary engines during the hotelling at berth during the year of 2019. Then, based on calculated emission inventory, this study suggested and simulated applicable green policies in the practice: (1) local emission control area realization, (2) vessel speed reduction program, (3) application of cold ironing, and (4) establishment of a national integrated emission platform. The combination of the three first policies could help reduce the significant volume of emitted CO (29%), NOx (30%), SOx (93%), PM10 and PM2.5 (64%), VOC (28%), NH3 (30%), and CO2 (30%).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".