The Enbridge Consumers Gas "Steam Saver" Program ("As Found" Performance and Fuel Saving Projects from Audits of 30 Steam Plants)
Bibliographic record
Abstract
In Canada, medium and large sized steam plants consume approximately 442 Billion Cubic Feet \n(12.5 Billion Cubic Meters) of natural gas annually. This is 25% of all natural gas delivered to all customers. (Small steam plants and Hydronic heating boilers consume another 15%) \nEnbridge Consumers Gas, a local gas distribution company located in Toronto, has approximately 400 Industrial and Institutional customers who own medium or large sized steam plants. \nDuring the past three years, Enbridge has developed a comprehensive steam energy efficiency program called "Steam Saver". This program is aimed at these 400 customers. The heart of this program is the boiler plant audit and performance test. \nThis paper describes the fuel saving results for more than 30 medium and large sized boiler plants where audits have been completed and projects have been implemented. \nThe savings in cubic feet per year of natural gas are broken down according to project or technology type. The financial payback is indicated for each category. \nEleven of the larger plants have been "benchmarked". Plant efficiency, fuel consumption, steam costs and other performance variables are tabulated for these plants.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".