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Record W4400695529 · doi:10.51270/47.1.23

Proteins and Our Past: An Exploration of Human Bone Protein from the Eighteenth-Century Fortress of Louisbourg, Nova Scotia, and Its Potential Applications in Bioarchaeological Research

2023· article· en· W4400695529 on OpenAlexvenueaboutno aff
Nicole Hughes, Amy B. Scott

Bibliographic record

VenueCanadian Journal of Archaeology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNova scotiaFortress (chess)Nova (rocket)HistoryHuman boneArchaeologyAncient historyEngineeringBiologyAeronautics

Abstract

fetched live from OpenAlex

Bioarchaeologists can further investigate human bone metabolism at the biomolecular level by incorporating biochemical methods into their research. Recently, there has been a focus on osteocalcin, an abundant non-collagenous bone protein, because of its clinically identified relationship with biological factors (i.e., age and sex), activity, and pathological conditions (i.e., disease). For this study, osteocalcin was extracted and quantified from the femora of 27 individuals from the Fortress of Louisbourg (1713–1758) skeletal collection to explore if the clinical relationship between osteocalcin and sex, age, activity, and pathological conditions can also be established in archaeological bone. However, no significant relationships between osteocalcin concentrations and biological factors (i.e., age and sex), activity, or pathological conditions were identified. This is the first study to quantify osteocalcin from archaeological human skeletal remains from a Canadian context and provides another example of how this method may be used to study stress in bioarchaeological populations.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.108
GPT teacher head0.352
Teacher spread0.245 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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