Bibliographic record
Abstract
Air emissions associated with port operations that impact local and regional air quality can be reduced through voluntary cooperative efforts. In 2007, several Puget Sound ports, regulatory agencies, and other organizations published the 2005 Puget Sound Maritime Air Emissions Inventory. A similar inventory was developed in British Columbia at about the same time. Following the publication of both inventories, Port Metro Vancouver, the Port of Seattle, and the Port of Tacoma ("the Ports"), along with affiliated regulatory agencies, used these inventories to produce the Northwest Ports Clean Air Strategy ("the Strategy") to manage and reduce port-related air emissions, mainly from diesel fuel combustion. Voluntary emission reduction initiatives and potential actions are defined in the Strategy for six sectors [rail, trucks, ocean-going vessels (OGV), cargo-handling equipment (CHE), harbor craft, and port administration]. Air emission reduction goals were set with near-term and long-term milestone years of 2010 and 2015, respectively. While the strategy outlines shared performance measures, each port is implementing emission reduction programs appropriate to its operations. In 2012, updated inventories were published which illustrate the successful results of emission reduction efforts. The updated emission inventories are now a foundation for an update of the Strategy with 2015 and 2020 goals.
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 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 | 0.000 |
| 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) | 0.062 | 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".