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Record W1695347809 · doi:10.1002/ajpa.22635

A re‐evaluation of the impact of radiographic orientation on the identification and interpretation of <scp>H</scp>arris lines

2014· article· en· W1695347809 on OpenAlexafffund
Amy B. Scott, Robert D. Hoppa

Bibliographic record

VenueAmerican Journal of Physical Anthropology · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaOdense Universitetshospital
KeywordsRadiographyIdentification (biology)Orientation (vector space)Interpretation (philosophy)High resolutionMedicineComputer scienceHistoryArchaeologyBiologySurgeryGeometryMathematicsEcology

Abstract

fetched live from OpenAlex

The identification of Harris lines through radiographic analysis has been well-established since their discovery in the late nineteenth century. Most commonly associated with stress, the study of Harris lines has been fraught with inconsistent identification standards, high levels of intra- and interobserver error, and the inevitability of skeletal remodelling. Despite these methodological challenges, the use of Harris lines remains an important contributor to studies of health in archaeological populations. This research explores the radiographic process, specifically orientation and how Harris lines are initially captured for study. Using the Black Friars (13th-mid 17th centuries) skeletal sample from Denmark, 157 individuals (134 adults; 23 subadults) were radiographically analyzed in both an anterior-posterior (A-P) and medial-lateral (M-L) view for the left and right radii and tibiae. Based on the current methodological standards within the literature, it was hypothesized that the A-P view would provide the best resolution and visualization of Harris lines. The results, however, show that the number of lines visible in the M-L view were significantly higher than those visible in the A-P view; inferring that the M-L view is superior for the study of Harris lines.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.032
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 head0.020
GPT teacher head0.317
Teacher spread0.297 · 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 designQualitative
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

Citations29
Published2014
Admission routes2
Has abstractyes

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