MétaCan
Menu
Back to cohort
Record W4401366649 · doi:10.1080/14614103.2024.2380117

Applications of Micro-CT Imaging in Age-At-Death Estimates of Maya Dogs

2024· article· en· W4401366649 on OpenAlexafffund
Miranda George, Elizabeth H. Paris, Wei Liu, Roberto López Bravo, Gabriel Laló Jacinto

Bibliographic record

VenueEnvironmental Archaeology · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Calgary
KeywordsEnamel paintScannerPulp (tooth)MolarDentistryGeologyArchaeologyMaterials scienceOrthodonticsGeographyMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

While there are many techniques for estimating age-at-death in archaeological dogs, the pulp cavity/tooth width ratio is considered one of the most accurate methods. This study adapts this technique for application to MicroCT imaging, a non-destructive methodology that is rapidly gaining ground in faunal analysis. Mandibular first molar and upper and lower canine teeth recovered from two Late Classic-Early Postclassic period sites in the Maya highlands, Moxviquil and Tenam Puente, were imaged using a SCANCO μCT35 MicroCT scanner. The widths of tooth roots, pulp cavities, cuspal enamel thicknesses, and enamel attrition measurements were then taken using both the scanner’s post-processing system and 3D Slicer, an open-access programme designed for imaging biomedical scans and other 3D files, and pulp cavity infilling ratios were calculated to obtain an age-at-death estimates in months for each specimen. Based on these, this study presents preliminary interpretations of canid age-at-death patterns at Moxviquil and Tenam Puente, including a range of juvenile specimens from a funerary cave context at Moxviquil.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.999

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 studies0.0000.028
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.237
Teacher spread0.227 · 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 designTheoretical or conceptual
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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueEnvironmental ArchaeologySame topicForensic Anthropology and Bioarchaeology StudiesFrench-language works237,207