Sustainable pub : energy
Bibliographic record
Abstract
This report contains the analysis and suggestions for improvements on Koerner’s Pub located on UBC’s Vancouver campus in terms of energy efficiency. The food industry is one of the highest consumers of energy in commercial businesses and as such, there is significant potential for energy savings. The primary stakeholder, Tim Yu, requested for an in depth research into possible low cost solutions that would aid Koerner’s Pub in lowering energy consumption. In focusing on the upstairs kitchen area, our team has done research in the following areas: lights, and energy efficient practices. In our investigation of the site (Koerner’s Pub) and through research of restaurants and pubs in general, we have compiled possible improvements that may be applied. Through our research and observation on the operation of the pub, we were able to conclude which appliances consumed the most energy and which ones were wasting energy. We have primarily focused on improving operational use of the walk-in refrigerator, stove, oven, and dishwasher. It was noted that there were certain operations that could be improved or eliminated, such as inefficient use of the refrigerator and the dishwasher. As for lighting, our investigation allowed us to observe the different lighting needs of the many areas of the pub. For example, areas such as the kitchen would need to be well lit while the main dining area should be dim and emit a warm atmosphere. The four light bulbs we investigated were incandescents, compact fluorescent lights (CFL), halogen lights, and light emitting diodes (LED). Certain factors we paid most attention to were environmental impacts, lifetime, initial cost, as well as light properties. Certain lamps could be potentially harmful if not disposed of correctly, some had very short life spans, most efficient lamps had high initial costs, and each lamp gives off its own colour and range and light. Aiming to be sustainable and energy efficient has many benefits, not only environmental but also economical. Without suggesting appliances or technologies that require a large initial investment by the primary stakeholder, we have concluded that cost effective light bulbs and small appliances/technologies and behavioural changes will provide the most effective reduction in energy consumption. For the upstairs kitchen, the use of CFLs (with proper disposal since they contain mercury) is recommended for their long lifespan and brightness, and LEDs are recommended for the bar and patio areas (energy efficient and long lifespan), while the main dining area continues to use incandescents for the warm light that is emitted, to maintain a pleasing atmosphere. In the kitchen area we highly encourage appliance operators to be aware of unnecessary use of energy as well as the use of interior vinyl strips on the walk-in refrigerators to help maintain the temperature of the refrigerators. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.193 | 0.099 |
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".