Differentiating Human Bone from Animal Bone: A Review of Histological Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.034 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it