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Record W2015162053 · doi:10.3138/jvme.34.4.416

Virtual Microscopy in a Veterinary Curriculum

2007· article· en· W2015162053 on OpenAlexvenueno aff
Michael H. Sims, Chamindrani Mendis-Handagama, Robert N. Moore

Bibliographic record

VenueJournal of Veterinary Medical Education · 2007
Typearticle
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual microscopyThe InternetComputer scienceMultimediaCurriculumTelepathologyMicroscopeWorld Wide WebMedicinePathologyPsychology

Abstract

fetched live from OpenAlex

Teaching faculty in the University of Tennessee College of Veterinary Medicine assist students in their professional education by providing a new way of viewing microscopic slides digitally. Faculty who teach classes in which glass slides are used participate in a program called Virtual Microscopy. Glass slides are digitized using a state-of-the-art integrated system, and a personal computer functions as the "microscope." Additionally, distribution of the interactive images is enhanced because they are available to students online. The digital slide offers equivalent quality and resolution to the original glass slide viewed on a microscope and has several additional advantages over microscopes. Students can choose to examine the entire slide at any of several objectives; they are able to access the slides (called WebSlides) from the college's server, using either Internet Explorer or a special browser developed by Bacus Laboratories, Inc.,(a) called the WebSlide browser, which lets the student simultaneously view a low-objective image and one or two high-objective images of the same slide. The student can "move the slide" by clicking and dragging the image to a new location. Easy archiving, annotation of images, and Web conferencing are additional features of the system.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.031
GPT teacher head0.378
Teacher spread0.347 · 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

Citations33
Published2007
Admission routes1
Has abstractyes

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