Ongoing Development of a Multi-User Emission Inventory GIS-Based Tool
Bibliographic record
Abstract
This paper presents an update on the current status of an ongoing project to develop a multi-user, Emission Inventory GIS-based tool (EIGIS). The fundamental goal behind the EIGIS is to allow a number of simultaneous users to create high-resolution area and mobile source emission inventories more efficiently. To accomplish this, EIGIS was developed using a bottom-up approach in which it is possible to produce updated or scenario inventories based on changes in science (e.g. emission factors), feature-based activity data (e.g. road traffic volumes, population density), and geography (e.g. changes in the location and/or magnitude of emissions that are based on physical characteristics of geographic features such as roads or land use). The tool can also be used to perform queries, generate reports and prepare model input files from computed emissions inventories. EIGIS underwent a rigorous design phase involving input and feedback from a number of different stakeholder groups. This was followed by the development of an initial prototype capable of defining and calculating emissions at the activity level in the Greater Vancouver Regional District in southern British Columbia. Since that time, a number of components within the tool have been upgraded and added, including a partial revision of the underlying database structure upon which the tool is based. A next generation version of the tool is now in the development and testing phase.
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".