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Record W4291124637 · doi:10.1111/1556-4029.15120

Comparative analysis of fetal pig decomposition processes in burials of variable depths and wrapping

2022· article· en· W4291124637 on OpenAlexaff
Megan A. Hill, James T. Pokines

Bibliographic record

VenueJournal of Forensic Sciences · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsOffice of the Chief Medical Examiner
FundersSchool of Medicine, Boston University
KeywordsDecompositionFetusSignificant differenceAnimal scienceBiologyMathematicsStatisticsEcologyPregnancy

Abstract

fetched live from OpenAlex

This research examined the effects that the variables of burial depth and presence of plastic wrapping had on the decomposition rate of fetal pig (Sus scrofa) remains in a New England environment. The decomposition of 56 fetal pigs was observed in four independent variable groups: 20 cm depth unwrapped, 20 cm wrapped, 60 cm unwrapped, and 60 cm wrapped, with exhumation at months 1, 2, 3, 6, 9, 12, and 18. The authors hypothesized that the rate of decay would be slower for wrapped remains and/or for remains at a greater burial depth. Analysis of these remains consisted of preburial and postburial mass, adipocere coverage, skeletal exposure, and decomposition quantified as Total Body Score (TBS). The difference between preburial and postburial mass was reported as a loss percentage to account for varying preburial masses. Wrapping was a significant influencer of mass loss percentage, with p = 0.0298 but not for the TBS, with p = 0.17565. Burial depth did not have a significant effect on either mass loss percentage or TBS, with p = 0.1956 and 0.08969, respectively. This study suggests that wrapping has a greater influence on decomposition patterns than burial depth in this environment, particularly the mass loss percentage. It is suggested that there are limitations with the use of TBS in Postmortem Interval (PMI) estimation, such as variable burial conditions and body characteristics.

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 score0.116
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.037
GPT teacher head0.298
Teacher spread0.261 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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