Data collection framework for enhanced carbon intensity indicator ( <scp>CII</scp> ) in the oil tankers
Bibliographic record
Abstract
Abstract The International Maritime Organization (IMO) aims to reduce greenhouse gas (GHG) emissions by 40% by 2030 compared with 2008. The carbon intensity indicator (CII) calculates the annual reduction factor required to continuously improve a ship's operational carbon intensity at a specific rating level. Verification and documentation of the achieved annual operational CII against the prescribed target are necessary to establish the operational carbon intensity rating. This study focuses on the intricate process of data collection for CII within the oil shipping industry, targeting engineering departments and shipboard management teams. Against the backdrop of the industry's substantial carbon dioxide emissions, the IMO has mandated the calculation of CII values for ships exceeding 5000 gross tons to promote sustainability and reduce environmental impact. We have collected emission data of 20 oil tankers over a period of 2 years using our ship maintenance and operating system (SMOS) and analyzed the data to compare the CII ratings. Our results indicate that a staggering ~63% of the vessels had the lowest CII rating of category E. It is therefore crucial to properly collect, organize, and evaluate data for CII calculation and take necessary measures to improve rating. This paper provides a deeper insight into the evolving CII calculation methodology, emphasizing the incorporation of correction factors and exclusions, and delineates the essential data collection practices needed to facilitate accurate CII calculations.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".