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Record W2318289201 · doi:10.7227/ijmee.29.1.5

Teaching the Environmental Impact of Industrial Processes

2001· article· en· W2318289201 on OpenAlex
Marc A. Rosen

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Mechanical Engineering Education · 2001
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIndustrial ecologyEnvironmental impact assessmentProcess (computing)EngineeringSecondary sector of the economyBusinessEngineering managementEnvironmental planningEcologyEnvironmental scienceComputer scienceEconomicsSustainabilityEconomy

Abstract

fetched live from OpenAlex

Concerns about the environmental impact of industrial processes will likely continue to increase in the future as global population grows, the demand for engineering-related goods and services expands, and economic development increases. In this paper, a recently developed course on the environmental impact of industrial processes is described, which is suitable for engineers in mechanical and other engineering fields and for others working in industry and technology. The course concerns such topics as industry—environmental interactions, industrial ecology, environmental concerns, life-cycle assessment, industrial process residues and design for environment. Two comprehensive case studies and a major student project are included in the course. The course has been offered twice. Based on the feedback received, the course has been successful in meeting its objectives of increasing student knowledge and appreciation of the environmental impact of industrial processes, and has been considered worthwhile by 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.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.338

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 head0.018
GPT teacher head0.296
Teacher spread0.279 · 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