MétaCan
Menu
Back to cohort
Record W2061230577 · doi:10.1002/ajpa.10397

Understanding muscle markers: Lower limbs

2004· article· en· W2061230577 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAmerican Journal of Physical Anthropology · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsMuscle massLower limbMedicineSample size determinationAnatomyDemographyInternal medicineSurgeryMathematics

Abstract

fetched live from OpenAlex

Musculoskeletal markers are frequently used to reconstruct past lifestyles and activity patterns. Yet the reliability of muscle marker measurements has been called into question because they may be confounded by body size. In this study, an aggregate muscle marker variable was calculated using 20 insertion sites (14 femoral, 6 tibial), and I examined their effects on lower limb size (as a proxy for body size), age, and sex. Analyses were made of a sample of 77 (57 males, 20 females) Native British Columbians (3,500-1,500 years BP) and 18th century Quebec prisoners. Muscle markers were measured using two-point observer rating scales; size was measured by standard methods; and age and sex were determined through pelvic, cranial, and dental morphology. Lower limb muscle markers correlated with: age, r=0.61; lower limb size, r=0.52; and sex, r=0.49; P <0.001. Older individuals had higher muscle marker scores, as did larger individuals and males. Based on partial correlations and regression analyses, age was the best overall predictor of lower limb muscle markers.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.078
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 head0.050
GPT teacher head0.281
Teacher spread0.231 · 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