Virtual Microscopy in a Veterinary Curriculum
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".