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Record W4393151465 · doi:10.1097/hp.0000000000001786

A Perspective from Ontario Tech University Industrial Research Chairs on 20 Years of Capacity Building in Health Physics and Radiation Science

2024· article· en· W4393151465 on OpenAlexaffabout
Edward Waller, Kirk Atkinson, A.J. Waker

Bibliographic record

VenueHealth Physics · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsHealth physicsHealth scienceNuclear sciencePerspective (graphical)Research programEngineering ethicsEngineering managementEngineeringBusinessMedical educationMedicinePhysicsRadiation protectionComputer scienceNuclear engineeringNuclear medicine

Abstract

fetched live from OpenAlex

ABSTRACT: Ontario Tech University (University of Ontario Institute of Technology) is one of Canada's newest universities, having been incorporated in 2002. In 20 y, the University has increased enrollment from a few hundred students to over 10,000. The University was designed to be "market driven" and as such offered courses that had high market demand. The Faculty of Energy Systems and Nuclear Science was one of the first faculties to be established at the University, with the intent to fill a gap between personnel that were retiring out of the nuclear industry and the dearth of nuclear engineers and health physicists being educated in Canada. As such, the University established unique programs in both nuclear engineering and health physics/radiation science with strong input from industry stakeholders. This paper will discuss the evolution of the Health Physics and Radiation Science program at Ontario Tech from the teaching and capacity building perspective, and it provides insight regarding health physics and radiation science research at Ontario Tech under the industrial research chair program.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.936

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 integrity0.0000.001
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 head0.143
GPT teacher head0.401
Teacher spread0.258 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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