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Record W4384833948 · doi:10.1002/9781119682691.ch14

The Use of Histology to Distinguish Animal from Human Burnt Bone with Reference to Some Limitations

2023· other· en· W4384833948 on OpenAlexaffabout
Pamela Mayne Correia, Kalyna Horocholyn, Kassandra Pointer

Bibliographic record

Venuenot available
Typeother
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHuman boneBone tissueAnatomyHistologyBone cellHard tissueBiologyPathologyMedicineDentistry

Abstract

fetched live from OpenAlex

Histological analysis of human bone tissue within the field of biological anthropology has had a long tradition for interpreting the bones origin and age, and prior to that within anatomy laboratories. This chapter presents an overview of the common tissue types found in animal bone (including human), and provides some qualitative comparisons for unmodified bone and some quantitative comparisons for unmodified bone. It also presents a case study of burnt bone at three temperatures. The study is drawn from several projects originating in the Department of Anthropology at the University of Alberta. Primary bone tissue can be described by three recognizable types. The first is known as fetal bone and is the earliest bone formed, woven bone. Secondary bone tissue is divided into two types: Haversian systems and non-Haversian lamellar bone. Research on the bone structures in domestic pigs confirms the artiodactyl pattern of predominantly plexiform bone with few scattered secondary osteons.

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 imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.002
Science and technology studies0.0010.004
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.186
GPT teacher head0.294
Teacher spread0.108 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes2
Has abstractyes

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