Learning about Energy Consumption Habits of our Peers and Advocating for Installation of Solar Panels in Our School
Bibliographic record
Abstract
Energy consumption has become an extremely prevalent problem in modern society. As the need for energy grows the impacts of that need begin to grow as well. My team surveyed students at Erindale Secondary School to inform ourselves on the usage of energy amongst high school students. The results from the study indicated that high school students use four to five hours of energy outside of school on weekdays, which is worrisome considering that students are at school for six hours a day. We found that over three hours per day are spent on computer by both boys and girls, and that boys spend more than 3 hours per day on game consoles. Considering that it would be harder for us to impact what our peers do at home, we decided to take action in our school. First, we wrote a letter to our school principal to request assessment of our school for installation of solar panels. The principal agreed to the idea. Then, we wrote a letter to Pure Energies asking the company if they would be interested in installing solar panels at our school. We received a reply from the Vice President informing us that his subcontractors have asked if they can help on this project. This was great news for us and we hope that our efforts will contribute to a more energy efficient school.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".