Bibliographic record
Abstract
Preservation techniques have evolved over the years to respond to the need of longer dissection periods, with formaldehyde being widely used for this purpose. In recent years, efforts have been focusing on reducing the health hazards of this fixative and the rigidity of the tissues embalmed with it. With every embalming technique that they are either developing or resurrecting from various protocols, institutions need to assess the fire and health hazards of all the chemicals being used. Compliance with the storage and handling safeguards listed in the Material Safety Data Sheet of each chemical, needs to be accompanied by infrastructure changes. To reduce the health hazards of formaldehyde, institutions are taking appropriate countermeasures directed at the source itself, by using substitutes or injecting formaldehyde chelating agents, and are adopting high performance air extraction systems to protect the users. However, little is known about the flammability risk of embalmed human bodies. During a recent visit to McGill University morgue by architects and safety inspectors to expand it, we were asked about the flammability risk of the embalmed specimens, so we carried out a flashpoint test to evaluate the flammability potential of specimens embalmed with each of our three embalming techniques: formaldehyde-based, Thiel, and phenol-based. With the phenol-based embalming showing the most risks, attention should be drawn to all institutions who are experimenting with various embalming techniques to evaluate the flammability potential of their embalmed specimens.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".