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Record W4393549232 · doi:10.18260/1-2--45532

Generative AI as an educational resource

2024· article· en· W4393549232 on OpenAlexaff
Stephen Strain, Andrew Watson, Matthew Hale

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicEngineering Education and Technology
Canadian institutionsColumbia College
Fundersnot available
KeywordsComputer scienceGenerative grammarResource (disambiguation)Artificial intelligence

Abstract

fetched live from OpenAlex

A. Blass Watson, a biomedical engineering doctoral candidate at the University of Memphis, received his bachelor's degree in biomedical engineering from Mississippi State University in 2016.He worked at the Social Therapeutic and Robotic Systems (STaRS) lab and the Center for Advanced Vehicular Systems (CAVS).His work involved redesigning systems to mimic biological counterparts, exploring new materials for rapid prototyping, and developing mechanical systems for robotics focused on human-computer interaction with both civilians and law enforcement.Since starting his studies at the University of Memphis in 2017, under Dr. Joel D. Bumgardner, he has delved into biomaterial development, nanoparticles, and additive manufacturing techniques like electrospinning, electrospraying, and 3D printing.His master's thesis, completed in December 2020, focused on "Electrosprayed Chitosan-calcium Phosphate Nanoshells Composite Coatings on Silanated Titanium Plates."Currently, he is pursuing his PhD, concentrating on 3D printing biological polymers for wound repair.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.081
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.006
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0810.015

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 head0.009
GPT teacher head0.288
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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