Bibliographic record
Abstract
This paper presents a bottom-up air emission inventory (AEI) for two ports on the Great Lakes. The details of every commercial vessel—US, Canadian, and Foreign Flag—and every visit to each port were cataloged for 2004. The actual open-lake speed, reduced speed to enter the port, the time to maneuver from the breakwall to the dock, and the times at the dock performing cargo operations were evaluated. Appropriate current emission factors for the type of propulsion engine and auxiliary engines for each vessel were used for the times in each mode to obtain total emissions. The Port of Cleveland, Ohio, is particularly important because that port was studied as part of the EPA's National Emission Inventory in 1999 and 2002, and those results were used as the marine transportation mode emissions baseline that is extrapolated to all other Great Lakes port states, cities, and counties based on port tonnages. The Port of Duluth, Minnesota, was chosen because it is primarily a shipping port as contrasted to Cleveland, which is primarily a receiving port. Vessel operations are quite different in each port. Using this detailed study and current emission factors, Great Lakes marine mode emissions are shown to be about one-half of original study estimates for Cleveland. The relative efficiency of the marine mode of transportation is reviewed for the Port of Cleveland. The Great Lakes vessels, ports, and trade patterns clearly show "We're different up here."
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".