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Record W2778179828 · doi:10.29173/comp46

The relationship between age and arachnoid depressions in humans

2017· article· en· W2778179828 on OpenAlexaffvenueabout
Rajitha Sivakumaran

Bibliographic record

VenueCOMPASS · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPathologicalSenescenceCranial vaultSkullAge groupsAnatomyBiologyMedicineDemographyPathologyInternal medicine

Abstract

fetched live from OpenAlex

The human skeletal collection housed in the Department of Anthropolog y at the University of Alberta was used to determine the relationship between age and the occurrence of arachnoid depressions on the endocranial aspect of the skull. There were significant differences between the total number of arachnoid depressions found on the vaults of juveniles, adolescents, and adults. When mean ages were compared with total number of arachnoid depressions on the vault, a significant relationship did not emerge. When age was grouped into nine-year intervals to counteract the effect of idiosyncratic variation, the mean number of depressions increased with age, as did the maximum number of arachnoid depressions. The frequency of older individuals without arachnoid depressions waslow. Older individuals were more likely to have larger and deeper arachnoid depressions. There were no sex-based differences in the expression of arachnoid depressions. There were no significant differences between archaeological, historic, and modern samples or between pathological and healthy individuals. Although this study verifies the association between arachnoid depressions and senescence, the presence of arachnoid depressions is highly variable andcannot be used reliably as an indicator of chronological age or even as a sign of senescence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.442

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 studies0.0010.000
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 head0.047
GPT teacher head0.330
Teacher spread0.283 · 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

Citations1
Published2017
Admission routes3
Has abstractyes

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