Data Management and Emissions Estimation for a Ports Landside Emission Inventory for the Vancouver Fraser Port Authority
Bibliographic record
Abstract
A Landside Emissions Inventory for over 90 marine terminals under the administration of the Vancouver Fraser Port Authority in British Columbia was completed in 2008. The Inventory is activity based, with estimates of common air contaminants, air toxics and fuel consumption at several different levels of resolution. Data collection was challenged by the wide spatial distribution of terminals in the region and the level of effort terminal managers were willing to expend. To minimize a prohibitive amount of ‘leg work ’ in capturing accurate data for cargo handling, trucking and rail activity, an excel questionnaire was developed and linked to an emissions inventory (EI) database. The EI database was configured with a matrix of EPA MOBILE and locomotive emission factors to link with trucking and rail activity input fields in the questionnaire. More significantly, the EPA NONROAD methodology and data tables were linked such that the NONROAD emissions model was mimicked internally, bypassing model defaults. The result was a largely automated, functional database EI model with routines to import a terminal’s activity information, complete all emissions calculations and conduct accuracy checks based on fuel criteria. In addition, the estimation routines were complemented so that use of alternative fuels (e.g., biodiesel) could be handled. The EI model minimized manual calculation efforts and allowed more time to be spent on dialogue with terminal operators to improve activity level estimates. The database was linked with the input terminal questionnaires, facilitating scenario testing and forecasting, which was a required component of the EI project.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".