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Record W1951854599 · doi:10.24908/pceea.v0i0.4701

The Role of Biology in an Engineering Program

2012· article· en· W1951854599 on OpenAlexaffvenueabout
Danny Mann

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBiological systems engineeringBiological engineeringSynthetic biologySystem of systems engineeringBiosystems engineeringComponent (thermodynamics)EngineeringEngineering ethicsComputer scienceSystems engineeringEngineering managementCivil engineering softwareBiologySystems designComputational biologyBioinformaticsPhysics

Abstract

fetched live from OpenAlex

Biology may not be a typical subject in the traditional engineering disciplines (i.e., civil, electrical, or mechanical), however, it is a fundamental component of disciplines such as biosystems engineering. Biosystems engineering emphasizes the application of engineering principles to biologically-based systems (i.e., systems that include plants, animals, microorganisms, or humans). A biosystems engineering program is designed to give students knowledge of the fundamental principles of engineering and to introduce biological concepts to enable these engineers to successfully interact with relevant professionals when solving engineering problems involving biological systems. The Department of Biosystems Enginering at the University of Manitoba offers five specializations (agricultural,biomedical, bioprocessing, environmental, and sustainable building systems) that can be taken within the biosystems engineering program. The purpose of this paper is to discuss the biology content of the biosystems engineering program at the University of Manitoba and to highlight the importance of biology to each of the five specializations.

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)0.000
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 score0.187
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.004
GPT teacher head0.201
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

Citations2
Published2012
Admission routes3
Has abstractyes

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