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Record W2410259403 · doi:10.1007/978-1-61779-977-8_15

Identification of Cooked Bone Using TEM Imaging of Bone Collagen

2012· article· en· W2410259403 on OpenAlexfundno aff
Hannah Koon

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
FundersYork University
KeywordsCharringBone matrixTransmission electron microscopyMatrix (chemical analysis)Collagen fibrilBiophysicsBone structureChemistryUltrastructureElectron microscopeMaterials scienceCrystallographyBiomedical engineeringComposite materialNanotechnologyAnatomyBiologyOpticsMedicine

Abstract

fetched live from OpenAlex

Mild heating (≤100° C, 1 h)-typical of cooking-does not lead to detectable changes in any biochemical parameter yet measured; consequently bones that have been cooked, but which have not reached a temperature that will induce charring go undetected. We have used a microscopy based approach to investigate changes in the organization of the bone protein, collagen, as it is heated, using bone from heating experiments, short term burials, and archaeological sites. The work has revealed that the presence of a mineral matrix stabilizes the collagen enabling the damage to accumulate, but preventing it from causing immediate gelatinization. Once the mineral is removed, the damage can be observed using appropriate visualization methods. This chapter describes the transmission electron microscopy (TEM) technique that has been used to detect cooked bone by visualizing minor heat-induced damage at the level of the collagen fibril.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.051
GPT teacher head0.395
Teacher spread0.344 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations4
Published2012
Admission routes1
Has abstractyes

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