MétaCan
Menu
Back to cohort

Effects of Decomposition on Gunshot Wound Characteristics: Under Moderate Temperatures with Insect Activity

2009· article· en· W2060504712 on OpenAlexaffabout
Lauren E. MacAulay, Darryl G. Barr, D.B. Strongman

Bibliographic record

VenueJournal of Forensic Sciences · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsSaint Mary's UniversityRoyal Canadian Mounted Police
Fundersnot available
KeywordsGunshot woundPoison controlDecompositionInjury preventionInsectSuicide preventionHuman factors and ergonomicsOccupational safety and healthMedical emergencyEmergency medicineMedicineBiologySurgeryEcologyPathology

Abstract

fetched live from OpenAlex

Previous studies document characteristics of gunshot wounds shortly after they were inflicted. This study was conducted to determine if the early stages of decomposition obscure or alter the physical surface characteristics of gunshot wounds, thereby affecting the quantity and quality of information retrievable from such evidence. The study was conducted in August and September, 2005 in Nova Scotia, Canada in forested and exposed environments. Recently killed pigs were used as research models and were shot six times each at three different ranges (contact, 2.5 cm, and 1.5 m). Under these test conditions, the gunshot wounds maintained the characteristics unique to each gunshot range and changes that occurred during decomposition were not critical to the interpretation of the evidence. It was concluded that changes due to decomposition under the conditions tested would not affect the collection and interpretation of gunshot wound evidence until the skin was degraded in the late active or advanced decay stage of decomposition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.258
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations26
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Forensic SciencesSame topicForensic Entomology and Diptera StudiesFrench-language works237,207