Differentiating Human Bone from Animal Bone: A Review of Histological Methods
Bibliographic record
Abstract
This review brings together a complex and extensive literature to address the question of whether it is possible to distinguish human from nonhuman bone using the histological appearance of cortical bone. The mammalian species included are rat, hare, badger, racoon dog, cat, dog, pig, cow, goat, sheep, deer, horse, water buffalo, bear, nonhuman primates, and human and are therefore not exhaustive, but cover those mammals that may contribute to a North American or Eurasian forensic assemblage. The review has demonstrated that differentiation of human from certain nonhuman species is possible, including small mammals exhibiting Haversian bone tissue and large mammals exhibiting plexiform bone tissue. Pig, cow, goat, sheep, horse, and water buffalo exhibit both plexiform and Haversian bone tissue and where only Haversian bone tissue exists in bone fragments, differentiation of these species from humans is not possible. Other primate Haversian bone tissue is also not distinguishable from humans. Where differentiation using Haversian bone tissue is undertaken, both the general microstructural appearance and measurements of histological structures should be applied. Haversian system diameter and Haversian canal diameter are the most optimal and diagnostic measurements to use. Haversian system density may be usefully applied to provide an upper and lower limit for humans.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".