MétaCan
Menu
Back to cohort
Record W3024228184 · doi:10.33137/jaste.v5i1.34269

Learning about Energy Consumption Habits of our Peers and Advocating for Installation of Solar Panels in Our School

2020· article· en· W3024228184 on OpenAlexvenueno aff
Tehjae Tsukada, Kate Sheppard, Ashley Jia Wen Yip

Bibliographic record

VenueJournal for Activist Science and Technology Education · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Robotics and Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsPrincipal (computer security)Action (physics)Energy (signal processing)Consumption (sociology)PsychologyInstallationEnergy consumptionMathematics educationPublic relationsMedical educationEngineeringSociologyPolitical scienceComputer scienceMedicineComputer securitySocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.297
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal for Activist Science and Technology EducationSame topicEducational Robotics and EngineeringFrench-language works237,207