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Record W4295063271 · doi:10.3390/biology11091321

Experimental Evidence of Bone Lesions Due to Larder Beetle Dermestes maculatus (Coleoptera: Dermestidae)

2022· article· en· W4295063271 on OpenAlexfundno aff
Damien Charabidzé, Vincent Lavieille, Thomas Colard

Bibliographic record

VenueBiology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsnot available
FundersUniversité de BordeauxSimon Fraser University
KeywordsDermestidaeBiologyLarvaPupaAnatomyZoologyOsteologyForensic entomologyEcology

Abstract

fetched live from OpenAlex

Dermestid beetles (Coleoptera: Dermestidae) are necrophagous insects feeding on mummified carcasses. After six to seven molts, the larvae stop feeding and dig pupation chambers to hide and safely evolve into adults. Such pupation chambers have already been observed on archaeological mammals’ bones, but the attribution and interpretation of these osteological lesions lack experimental evidence in a forensic context. To observe whether dermestid larvae dig pupation chambers in human bones, 20 or 40 Dermestes maculatus (De Geer, 1774) larvae were placed in a dermestarium with different types of bones varying in species (Bos taurus or human), age (adult or immature), and preservation method (fresh or dry). Our results show that dermestid larvae caused multiple lesions, including larval mandible traces on cortical bone, cortical perforations, drilling of pupation chambers, destruction of the trabecular network, and the perforation of cartilage. Bone destruction was mainly observed on aged dry bones, while fresh bones only exhibited soft tissue and superficial cartilage lesions. According to these results, pupation chambers could indicate the simultaneous presence of several corpses at different decomposition stages, or the addition of new corpses while others were already skeletonized. These conclusions are particularly important in the case of mass graves, where chronology is sometimes difficult to establish.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.039
GPT teacher head0.287
Teacher spread0.249 · 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 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

Citations19
Published2022
Admission routes1
Has abstractyes

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