MétaCan
Menu
Back to cohort
Record W2104717717 · doi:10.24908/pceea.v0i0.3920

ADVANCED TOPICS IN ENVIRONMENTAL DESIGN ENGINEERING - A MULTI-DISCIPLINARY GRADUATE COURSE FOR FACULTY OF ENGINEERING AT UNB

2011· article· en· W2104717717 on OpenAlexaffvenue
Liuchen Chang

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsDisciplineInformatics engineeringEngineeringHealth systems engineeringMultidisciplinary approachEngineering educationEngineering managementEnvironmental engineering scienceBiological systems engineeringEngineering ethicsCurriculumCivil engineeringCivil engineering softwarePolitical scienceSociologyEarth sciencePedagogy

Abstract

fetched live from OpenAlex

The Faculty of Engineering at University of New Brunswick has developed a Master of Engineering (M.Eng.) degree program with concentration in environmental studies for graduate students in Civil Engineering, Chemical Engineering, Electrical and Computer Engineering, Forestry Engineering, Geology and Geomatics Engineering and Mechanical Engineering. One of the common courses of this graduate program is a newly-developed multidisciplinary course of Advanced Topics in Environmental Design Engineering. The objective of this course is to provide students with broad exposure of topics in environmental design engineering through lectures, case studies, field trips and projects. The lectures consist of 10 in-class modules delivered by industrial and academic guest lecturers in various technical fields of environmental engineering. This course covers multi-disciplinary topics including: Pollution and Pollution Control, Advanced Energy Systems, Renewable Energy, Life Cycle Assessment, Environmental Impact Assessment and Environmental Management, Sustainable Development. This course has been available to graduate students since 2003.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.705

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 head0.025
GPT teacher head0.223
Teacher spread0.198 · 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 designObservational
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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the Canadian Engineering Education Association (CEEA)Same topicSmart Materials for ConstructionFrench-language works237,207