Efficiency in YYC: Visualizing The City of Calgary's Energy Consumption
Bibliographic record
Abstract
To combat the alarming global issue of climate change, the City of Calgary has been leading the way in implementing clean energy programs to reduce its greenhouse gas emissions. For example, the transit system's LRT trains are now fully powered by wind power, and a growing solar power system delivers electricity to its municipal buildings (Dodge and Thompson, 2016). In a dramatic initiative that is observable from space, Calgary has recently completed its conversion of 80,000 streetlights, which used to account for 20% of the city's total energy consumption in 2012, to 50% more efficient LED lamps (The City of Calgary, 2018c; Rieger, 2015). In addition to the environmental grounds for these innovations, the city will enjoy economic benefits not only through direct energy savings, but also on the incoming carbon taxes imposed by the governments of Alberta and Canada. The City of Calgary continues planning for its future with ambitious goals of reducing city-wide greenhouse gas emissions by 20% below 2005 levels before 2020 and by 80% before 2050 (The City of Calgary, 2018a). We aim to explore and visualize its actual consumption rate by taking advantage of an open dataset recently released by the city. In this table, the monthly energy consumption is reported for of each of Calgary's facilities and infrastructure components over the past nine years (The City of Calgary, 2018b). This is an informative and unique dataset as comprehensive data at this level of detail and frequency are not publically available from other major Canadian cities or even for residential and private sector properties.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".