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Record W2234692706 · doi:10.3138/jvme.0315-035r1

Teaching Veterinary Histopathology: A Comparison of Microscopy and Digital Slides

2016· article· en· W2234692706 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Veterinary Medical Education · 2016
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual microscopyTelepathologyMicroscopyMicroscopeDigital pathologyHistopathologyVeterinary medicineComputer scienceMultimediaPathologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

Virtual microscopy using digitized slides has become more widespread in teaching in recent years. There have been no direct comparisons of the use of virtual microscopy and the use of microscopes and glass slides. Third-year veterinary students from two different schools completed a simple objective test, covering aspects of histology and histopathology, before and after a practical class covering relevant material presented as either glass slides viewed with a microscope or as digital slides. There was an overall improvement in performance by students at both veterinary schools using both practical formats. Neither format was consistently better than the other, and neither school consistently outperformed the other. In a comparison of student appraisal of use of digital slides and microscopes, the digital technology was identified as having many advantages.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.030
GPT teacher head0.337
Teacher spread0.307 · 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