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Record W2602946325 · doi:10.5206/wurjhns.2016-17.9

Western Faculty Profile: Dr. Vojislava Grbic

2016· article· en· W2602946325 on OpenAlexaffvenue
Dana Nguyen

Bibliographic record

VenueWestern Undergraduate Research Journal Health and Natural Sciences · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Productivity and Crop Improvement
Canadian institutionsWestern University
Fundersnot available
KeywordsLibrary scienceBiologyComputer science

Abstract

fetched live from OpenAlex

Dr. Vojislava Grbic is an Associate Professor in the Department of Biology at Western University. She completed her BSc in Plant Breeding and her MSc in Plant Genetics at the University of Novi Sad, before going on to complete her PhD in Genetics at the University of Wisconsin. She teaches undergraduate science courses, including Genetic Engineering and a fourth year Seminar in Genetics. Her research focuses on Arabidopsis developmental genetics, genomics of plant-pest interaction and biotechnology. Dana Nguyen, a member of the Academic Affairs Committee for WURJHNS, had the pleasure of interviewing Dr. Grbic to learn more about her career path and her research.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.389
Threshold uncertainty score0.871

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.3890.214

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 head0.105
GPT teacher head0.385
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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes2
Has abstractyes

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