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Framework for Interdisciplinary Power Systems Education for Engineering and Environment Students

2024· article· en· W4402475583 on OpenAlexaff
Jessie Ma

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEngineering educationPower (physics)Computer scienceEngineering managementMathematics educationSystems engineeringEngineering ethicsSoftware engineeringEngineeringPsychology

Abstract

fetched live from OpenAlex

A new cross-faculty course was developed for engineering and environment students at the University of Waterloo. This offers an interdisciplinary learning experience in line with the institution’s vision for collaboration in addressing the world’s most pressing challenges, including the climate catastrophe. The course aimed to equip students to better appreciate the interconnectedness and complexity of real-world energy projects. The STEEP framework was created to aid students to systematically consider social, technical, environmental, economic, and policy factors. A case study in using the STEEP model for wind power is presented. Overall, students responded with positive feedback and appreciated the structure that the STEEP framework provided. Students applied the STEEP framework in a couple of group assignments. Planned improvements for future course offerings include: more technical content; streamlined readings; short reflection papers; soft skill training; and increased interaction between engineering and environment students.

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)0.000
metaresearch head score (Gemma)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.006
GPT teacher head0.255
Teacher spread0.250 · 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
GenreMethods

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

Citations1
Published2024
Admission routes1
Has abstractyes

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