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Record W3031055464 · doi:10.1017/aaq.2020.29

Revisiting Bone Grease Rendering in Highly Fragmented Assemblages

2020· article· en· W3031055464 on OpenAlexaff
Eugène Morin

Bibliographic record

VenueAmerican Antiquity · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsGreaseRendering (computer graphics)ArchaeologyFragmentation (computing)ExcavationGeologyComputer scienceGeographyComputer graphics (images)Materials scienceComposite material

Abstract

fetched live from OpenAlex

Bone grease rendering is a low-return activity well described in the ethnohistorical and ethnographic literature. However, identifying this activity in archaeological contexts is complex because diagnostic criteria are few. The goals of this article are twofold: (1) to provide new experimental data on bone grease manufacture for assemblages associated with severe fragmentation, and (2) to assess how these data can be used to make stronger inferences about skeletal fat processing in the archaeological record. The results presented here show that, despite some variation, several forms of damage appear to be diagnostic of bone grease manufacture, regardless of the degree of fragmentation. The results indicate that extensive pounding produces many fragments that can be identified as deriving from articular ends, which conflicts with the oft-cited notion that articular ends are destroyed “beyond recognition” during this activity. Consequently, assemblages with few epiphyseal remains are not consistent with bone grease rendering, assuming that the comminuted fragments were not burned or discarded off-site after boiling. Because bone grease manufacture produces many small fragments, a close analysis of the indeterminate remains is strongly recommended, as is the use of fine mesh screens (2 mm or smaller) in excavations.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.009
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.039
GPT teacher head0.273
Teacher spread0.235 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations20
Published2020
Admission routes1
Has abstractyes

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