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Record W171319364 · doi:10.5206/uwoja.v21i1.8936

Interpreting Stable Carbon and Nitrogen Isotope Ratios in Archaeological Remains: An Overview of the Processes Influencing the δ13C and δ15N Values of Type I Collagen

2013· article· en· W171319364 on OpenAlexaff
Alexander Leatherdale

Bibliographic record

VenueThe University of Western Ontario Journal of Anthropology · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsWestern University
Fundersnot available
KeywordsStable isotope ratioIsotope analysisIsotopes of carbonIsotopes of nitrogenIsotopeArchaeologyδ13CInterpretation (philosophy)δ15NGeologySedimentary depositional environmentChemistryPaleontologyHistoryEnvironmental chemistryComputer scienceTotal organic carbonPhysics

Abstract

fetched live from OpenAlex

The application of isotopic ratio mass spectrometry to archaeological science has produced many important contributions to the study and understanding of ancient human and animal populations. Paleodietary reconstruction through the analysis of stable isotope ratios in skeletal, dental, and soft tissue remains presents another avenue for interpreting the past. The methodology employed to obtain isotopic data from archaeological remains directly influences the types of questions that can be addressed and the interpretation of the data. Furthermore, there are fundamental idiosyncrasies of archaeological specimens and their ante- and post-mortem environments that may influence the results of an isotopic study. This paper explores the ways in which the stable isotopic signatures of carbon and nitrogen in type I collagen in archaeological bones and teeth are formed, modified, or destroyed throughout life and in the post-depositional environment. For a comprehensive review of the methodological and interpretive implications of paleodietary reconstruction using stable isotopic analysis, see Ambrose (1993).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.228
Threshold uncertainty score1.000

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 studies0.0000.003
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 head0.024
GPT teacher head0.226
Teacher spread0.202 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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