Bibliographic record
Abstract
The art and science of the scalpel is learned through long hours of study and hands-on dissection. Starting with the lowly frog, students learn the basics of anatomy by practising on the real thing. But the Internet may change that. It now offers a “virtual” way to slice and dice everything from your favourite amphibian to the human body. One of the first sites developed was The Interactive Frog Dissection (teach.virginia.edu/go/frog/). Launched 7 years ago, it is aimed at high school level students and offers photos, illustrations and short videos of actual frog dissections. There are also some interactive sessions that ask the student to choose the right incision points. Staying with frogs, the Lawrence Berkeley National Laboratory has created The Virtual Frog Dissection Kit (www-itg.lbl.gov/frog). Students can interactively dissect a digital frog named Fluffy, create movies and play the Virtual Frog Builder Game. It challenges users to reconstruct a frog from the nerves up — a twist on the normal dissection process. But frogs are only one of the virtual creatures available for bloodless dissection. Students can also tackle the wonders of a cow's eye (www.exploratorium.edu/learning_studio/cow_eye/index.html) or delve into the marvels of a sheep's brain at the University of Scranton (academic.uofs.edu/department/psych/sheep/). The Berkeley High School offers whole-cat dissection (www.bhs.berkeley.k12.ca.us/departments/Science/anatomy/cat/index.html), while over at the Virtual Pig Dissection site (mail.fkchs.sad27.k12.me.us/fkchs/vpig/) students can cut into an electronic hog. Of course, the ultimate training ground for medical students is the human body, and the Internet serves up a wealth of virtual cadavers. The Virtual Autopsy at Leicester University (www.le.ac.uk/pathology/teach/va/) is aimed at the pre-clinical medical student. Pupils have 12 autopsies to perform, and are asked to determine the cause of death in each case. At the high end of these online offerings is the Visible Human Project at the National Library of Medicine (www.nlm.nih.gov/research/visible/). Developers here have been building a complete digital database of human anatomy since 1989. The result is a virtual male and female data set built from actual CT, MRI and cryosection scans. Access to the entire database requires a licence, but some online samples are available.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.421 | 0.311 |
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".