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Record W7106019166 · doi:10.7939/83135

A preliminary exploration of interpopulation variation in facial soft tissue thicknesses in Indigenous Western Canada.

2025· dissertation· en· W7106019166 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousPopulationWhite (mutation)Variation (astronomy)Table (database)Range (aeronautics)

Abstract

fetched live from OpenAlex

Targeted action to question, reexamine, and improve current forensic techniques and capabilities is needed to address the ongoing national crisis of Missing and Murdered Indigenous Persons. While a number of authors have suggested the application of population-specific facial soft tissue thickness (FSTT) tables is necessary when completing a facial approximation, others advocate for the application of universal standards. At the time of publication, there are no existing FSTT tables for the Indigenous populations of Western Canada which has resulted in the application of reference standards specific to other populations. This raises the important question: how important are population-specific reference standards? To evaluate whether there is an advantage to population-specific models, this study considered C-Table reference data representing Black American (Manhein et al., 2000), White American (Manhein et al., 2000), and Caucasoid (Australian; Stephan and Preisler, 2018; Stephan and Sievwright, 2018) populations. The results of a k-means clustering analysis (k = 4, sil = 0.14) of these data, in conjunction with additional testing examining FSTT variation by population, suggested that it is possible there is variation within the table that is attributable to biogeographic ancestry, sex, and body mass index (BMI). Next, original data from a Western Canadian Indigenous population (including self-identifying First Nations and Métis participants, n = 10) was collected and compared FSTT tables originating from other North American populations, including Mi’kmaq (Peckmann et al., 2015), Black American (Manhein et al., 2000), White American (Manhein et al., 2000), and Southwestern Native American (Rhine, 1983 cited in Taylor, 2001) groups. Data for the Western Canadian Indigenous sample (n = 10) were collected using the Philips Epiq 7c ultrasound system with a Philips L12-3 broadband linear array transducer (12 - 3 MHz). Limited evidence of significant interpopulation variation was found. Additionally, it was found that the Western Canadian Indigenous FSTT values are not likely to be significantly different from the 2023 T-Table, which is a compilation of existing reference data representing > 130 past FSTT studies (Hona and Stephan, 2023). Intrapopulation variation within the Western Canadian Indigenous sample was also considered and it was found that there is evidence of significant variation attributable to BMI but not age. Multidimensional scaling (MDS) plots were used to visualize the complex spatial relationships between Western Canadian Indigenous individuals, which were interpreted using known population histories and self-identified ancestry to provide context. Across each section of this work, limited evidence of true biogeographic variation in FSTT was found. However, this study introduced a novel framework for exploring Indigenous ancestries in a colonial context using relationality as an indigenist research framework. Future researchers are encouraged to continue to explore decolonial strategies for exploring and describing Indigenous ancestries.

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)0.002
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.210
Teacher spread0.197 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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