Bibliographic record
Abstract
The Queen’s Residence Energy Challenge (QREC) is an energy conservation initiative taking place in the residence halls at Queen’s University coordinated by the AMS Sustainability Office and the Sustainability Coordinator for Student Affairs . It is a two-part competition. Part one of the competition is an interresidence competition in which each residence hall will be competing to reduce their energy expenditures. Energy use will be compared to the corresponding time in the previous year, and the residence that reduces their energy use by the highest percentage will win the competition. This part of the project is being organized by members of the AMS Sustainability Office and the Sustainability Coordinator (Office of Student Affairs), and Residence Life staff and floor dons are helping execute it. The second part of the competition is an inter-university pledge drive, in which residents will be encouraged to sign a pledge stating that they will be participating in the QREC. Queen’s will be competing against the Universities of Waterloo and Guelph, and the school that has the highest percentage of residents participating will win a set of solar panels as a symbol of energy conservation and renewable energy. This part of the project is being coordinated by the Sierra Youth Coalition who has obtained funding from the Ontario Power Authority. The goals of the QREC are to reduce overall energy use in the residences, help students living in residence learn about their own energy consumption and ways to reduce it, and create a culture of sustainability at Queen’s. In this presentation I will go through the overall timeline of executing and planning the project, provide examples of ways to reduce energy consumption in residence, and provide results of both parts of the competition.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.093 | 0.026 |
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".