ADVANCED TOPICS IN ENVIRONMENTAL DESIGN ENGINEERING - A MULTI-DISCIPLINARY GRADUATE COURSE FOR FACULTY OF ENGINEERING AT UNB
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.032 |
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".