A test of the revised Frost's ‘rapid manual method’ for the preparation of bone thin sections
Bibliographic record
Abstract
A recent publication by Maat et al. (2001) introduces a modification of Frost's earlier ‘rapid manual method’ for ground bone thin section preparations, which uses ‘surface embedding’ with cyanoacrylate for sample protection. This revised method is said to provide a quick, inexpensive system for producing thin sections from archaeological specimens with a finished quality equivalent to more involved and equipment-intensive methods. Our study conducted a test comparing Maat et al.'s method with the standard technique that uses vacuum embedding media. A number of samples were tested, including modern bone samples from the dissecting room as well as archaeological samples in differing states of preservation. The results were highly favourable for a large majority of the specimens. For both modern and archaeological bone, Maat et al.'s revised method produced images of equivalent quality to samples prepared using embedding media. However, poor preservation of the specimens is still an issue, and only relatively dense, intact specimens hold up to the physical demands of the manual grinding procedure. This paper also adds a number of refinements to Maat et al.'s methodology. Future refinement of this technique would greatly facilitate large-scale sampling and encourage more osteological researchers to use histomorphometric analysis of archaeological hard tissues. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.053 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".