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The Influence of Clothing and Wrapping on Carcass Decomposition and Arthropod Succession: A Winter Study in Central South Africa

2008· article· en· W1975662090 on OpenAlexaffvenue
J.A. Kelly, T. C. van der Linde, Gail S. Anderson

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsSimon Fraser University
FundersMedical Research CouncilUniversiteit van die Vrystaat
KeywordsEcological successionArthropodClothingDecompositionGeographyEcologyBiologyArchaeology

Abstract

fetched live from OpenAlex

Clothing and wrapping of a carcass creates an environment which may influence natural decomposition and arthropod succession. Six pig carcasses were divided into three sample groups, each with a clothed carcass wrapped in a sheet and a carcass wrapped but with no clothes. These were sampled daily, or after five and ten days. An additional two pig carcasses, one with no clothes or wrapping, the other with clothes and no wrapping, were also sampled daily as controls. There was a delay in oviposition on the exposed and wrapped carcasses of five and nine days, respectively. During the extended active stage of the decomposition process, there was not a clear successional pattern, possibly due to the lack of competition between the arthropods. Overall the wrapped carcasses retained a higher mass than the exposed carcasses, as they remained moist for a longer period of time. The Diptera species breeding on the carcasses included the Calliphoridae, Lucilia spp., Chrysomya chloropyga and Calliphora vicina, and Sarcophagidae spp. The Coleoptera community was dominated by adult Dermestes maculatus (Dermestidae) and Necrobia rufipes (Cleridae) which were present on the carcasses throughout the trial. During the more advanced stages of decomposition, Thanathopilus micans (Silphidae) larvae, succeeded by D. maculatus larvae, were present.

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)0.000
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 score0.038
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.245
Teacher spread0.225 · 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

Citations37
Published2008
Admission routes2
Has abstractyes

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