Development of an interactive online educational resource for histology (725.15)
Bibliographic record
Abstract
The Department of Biomedical and Molecular Sciences developed an online educational resource called Scalable Gross Anatomy and Histology Image Catalogue (SGAHIC). This resource is a comprehensive database of approximately 10,000 gross anatomical specimens and histology images. SGAHIC uses virtual microscopy technology, which serves to emulate a full range of traditional microscope functionality. The purpose of this initiative was to innovatively enhance the utility of SGAHIC histology, with focus on the facilitation of teaching and learning. Histology Tutorial, using virtual microscopy, is an interactive resource developed to assist students with identifying organs and their respective tissues at the microscopic level. The implementation of self‐assessment modules encompassing labeled images, identification, and short‐answer questions, emphasize theory underlying the histology. Students are able to make connections between structures and functions, allowing them to achieve a deeper level of understanding. It is also designed to provide students with an immediate reinforcement of knowledge, which supports the learning process. This enhancement of SGAHIC has been received positively by students studying histology as well as faculty teachers. It is our hope that Histology Tutorial will promote a learning‐centered environment, enabling students to reach beyond surface learning of histology.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".