Advanced Emissions Profiling of Canadian Icebreaker Vessels: Bridging Calculations, Validating Empirical Data, and Exploring Decarbonization Strategies
Bibliographic record
Abstract
Accurate emissions monitoring of icebreaking vessels is necessary for evaluating compliance with maritime sustainability goals set by the International Maritime Organization (IMO). Traditional Tank-to-Wake (T2W) estimation methods rely on fuel consumption and fixed emission factors but often fail to reflect real-time variations caused by changing engine loads and operational profiles. This study introduces an empirical correction methodology to enhance T2W estimates using correction factors derived from onboard exhaust gas measurements. Data were collected using a portable Testo 350 analyzer aboard the Canadian Coast Guard Ship (CCGS) Sir Wilfrid Laurier, measuring emissions during selected operational instances. Correction functions for carbon dioxide (CO2), nitrogen oxides (NOx), and carbon monoxide (CO) were developed based on engine load and validated using stratified cross-validation. The corrected estimates demonstrated significantly improved alignment with measured values, especially under dynamic operating conditions such as cruising and maneuvering along with hotelling. This approach provides a practical and scalable pathway for improving emissions accuracy on vessels lacking continuous monitoring systems, supporting better alignment with regulatory reporting and decarbonization goals.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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 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".