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Record W2767158494 · doi:10.21083/surg.v9i2.4083

Five selected abstracts from ASCI*4010: Arts and Sciences Honours Research Seminar

2017· article· en· W2767158494 on OpenAlexaffvenueabout
Lori Canes, Zachary De Rose, Meghan C. Doherty, Shane Liquornik, Nailah Ramsoomair

Bibliographic record

VenueSURG Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThe artsBachelorCapstoneMedical educationLibrary scienceMathematics educationPsychologyComputer scienceMedicineVisual artsPolitical scienceArt

Abstract

fetched live from OpenAlex

This series features a selection of five abstracts from projects completed as part of the Winter 2017 iteration of ASCI*4010: Arts and Sciences Honours Research Seminar, a capstone course for students enrolled in the Bachelor of Arts and Sciences (BAS) program. The course is designed to provide students with an opportunity to integrate their research interests in the arts and sciences and to produce an extended, interdisciplinary term paper under faculty supervision. Students enrolled in the course are also given the chance to share their investigations with classmates and with the University of Guelph’s larger research community through a day of poster presentations. The abstracts collected here attest both to the interdisciplinary spirit of the BAS program and the varied research interests of its 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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.051
GPT teacher head0.345
Teacher spread0.294 · 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 designOther design
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

Citations0
Published2017
Admission routes3
Has abstractyes

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