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Record W2061683152 · doi:10.1002/ajhb.10021

Prediction of cross‐sectional geometry from metacarpal radiogrammetry: A validation study

2001· article· en· W2061683152 on OpenAlexafffundabout
Richard A. Lazenby

Bibliographic record

VenueAmerican Journal of Human Biology · 2001
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsUniversity of Northern British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLinear regressionRegression analysisCortical boneRegressionMathematicsStatisticsSample (material)Sample size determinationOrthodonticsMedicineAnatomy

Abstract

fetched live from OpenAlex

Regression models have been developed to adjust algebraic estimates of second metacarpal cortical bone geometry to actual values (as determined through invasive analysis). These models, derived from an archaeological sample of European origin, have high efficacy in predicting actual values but have not been validated on non-European samples. This paper reports a validation study for these models applied to a historic/proto-historic sample of Inuit from the central Canadian Arctic (n = 166; ages and sexes pooled as per the original study). In that the Inuit sample has been argued to exhibit distinct skeletal biology, this represents a robust test of the predictive models. The algebraic models again produced biased overestimates of actual values, whereas the predictive regression models were found to provide good estimates of actual values for measures of bone strength (Total Area, bending about the Ix and Iy axes), but not for estimates of mass (Cortical Area). This difference may exist in either functional or systemic differences in skeletal physiology and aging bone loss in the Inuit.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.086
GPT teacher head0.361
Teacher spread0.276 · 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.

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

Citations6
Published2001
Admission routes3
Has abstractyes

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