Black carbon emissions from in-use ships: a California regional assessment
Bibliographic record
Abstract
Abstract. Black carbon (BC) mass emission factors (EFBC; g-BC (kg-fuel)−1) from a variety of ocean going vessels have been determined from measurements of BC and carbon dioxide (CO2) concentrations in ship plumes intercepted by the R/V Atlantis during the 2010 California Nexus (CalNex) campaign. The ships encountered were all operating within 24 nautical miles of the California coast and were utilizing relatively low sulphur fuels. Black carbon concentrations within the plumes, from which EFBC values are determined, were measured using four independent instruments: a photoacoustic spectrometer and a particle soot absorption photometer, which measure light absorption, and a single particle soot photometer and soot particle aerosol mass spectrometer, which measure the mass concentration of refractory BC directly. The measured EFBC have been divided into vessel type categories and engine type categories, from which averages have been determined. The geometric average EFBC, determined from over 71 vessels and 135 plumes encountered, was 0.31 g-BC (kg-fuel)−1. The most frequent engine type encountered was the slow speed diesel (SSD), and the most frequent SSD vessel type was the cargo ship sub-category. Average and median EFBC values from the SSD category are compared with previous observations from the Texas Air Quality Study (TexAQS) in 2006, during which the ships encountered were predominately operating on high sulphur fuels. There is a statistically significant difference between the EFBC values from CalNex and TexAQS for SSD vessels and for the cargo and tanker ship types within this engine category. The CalNex EFBC values are lower than those from TexAQS, suggesting that operation on lower sulphur fuels is associated with smaller EFBC values.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".