Impact of Fuel Quality Regulation and Speed Reductions on Shipping Emissions: Implications for Climate and Air Quality
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
Atmospheric emissions of gas and particulate matter from a large ocean-going container vessel were sampled as it slowed and switched from high-sulfur to low-sulfur fuel as it transited into regulated coastal waters of California. Reduction in emission factors (EFs) of sulfur dioxide (SO₂), particulate matter, particulate sulfate and cloud condensation nuclei were substantial (≥ 90%). EFs for particulate organic matter decreased by 70%. Black carbon (BC) EFs were reduced by 41%. When the measured emission reductions, brought about by compliance with the California fuel quality regulation and participation in the vessel speed reduction (VSR) program, are placed in a broader context, warming from reductions in the indirect effect of SO₄ would dominate any radiative changes due to the emissions changes. Within regulated waters absolute emission reductions exceed 88% for almost all measured gas and particle phase species. The analysis presented provides direct estimations of the emissions reductions that can be realized by California fuel quality regulation and VSR program, in addition to providing new information relevant to potential health and climate impact of reduced fuel sulfur content, fuel quality and vessel speed reductions.
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 | 0.001 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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