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Record W2936383248 · doi:10.1371/journal.pone.0214575

Spondylosis deformans as an indicator of transport activities in archaeological dogs: A systematic evaluation of current methods for assessing archaeological specimens

2019· article· en· W2936383248 on OpenAlexafffund
Katherine J Latham, Robert J. Losey

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of Alberta
FundersEuropean Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsEtiologyMedicineArchaeologyPathologyGeography

Abstract

fetched live from OpenAlex

Over the past several decades archaeologists have used the spinal pathology spondylosis deformans as an indicator that archaeological dogs were used to pull or carry loads. This interpretive approach is largely based upon observations of prehistoric dog remains and archaeologist's interpretations of veterinary literature on recent sled dogs and other draft animals. However, no comparative large-scale studies of the occurrence of spondylosis deformans in wild canids, transport dogs, and dogs never involved in pulling or carrying loads have been published. To evaluate the reliability of spondylosis deformans in archaeological dogs as an indicator of participation in transport activities, 136 modern non-transport dogs, 19 sled dogs, and 241 wolves were systematically analyzed for the occurrence of spondylosis deformans. Our results indicate this pathology is not a reliable skeletal indicator of dog transport because the disease is prevalent in both dogs and wolves, regardless of their occupational histories. Numerous factors correlate with the occurrence and manifestation of this disease in canids, including age, body size, sex, and inbreeding. As such, it remains extremely challenging to identify specific etiologies for spondylosis deformans in archaeological specimens.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.273
GPT teacher head0.436
Teacher spread0.163 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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