Bibliographic record
Abstract
The Internet Mummy Picture Archiving and Communication Technology (IMPACT) radiological and context database, is a large-scale, multi-institutional, collaborative research project devoted to the digital preservation and scientific study of mummified remains, and the mummification traditions that produced them, using non-destructive medical imaging technologies. Owing to the importance of non-destructive analyses to the study of mummified human remains, the IMPACT database, website, and wiki provide a basis for anthropological and palaeopathological investigations, grounded in the most current technological imaging and communication standards, accessible through any internet connection, and protected against rapidly changing media standards. Composed of paired online radiographic and contextual databases, the IMPACT project is intended to provide researchers with large-scale primary data samples for anthropological and palaeopathological investigations. IMPACT addresses the limitations of the case-study approach to mummified human remains and contributes to the development of standards of practice in imaging of mummified remains. Furthermore, IMPACT allows researchers a greater appreciation of, and engagement with, patterns of health and disease in ancient times as well as the variability present in the mummification traditions of ancient Egypt and other cultures that sought to preserve their dead for eternity.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".