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Record W4390670303 · doi:10.1063/5.0189094

Effect of muzzle gases on forward spatter of viscoelastic blood in close-range shooting

2024· article· en· W4390670303 on OpenAlexaboutno aff
Jungwoo Huh, Seongdong Kim, Boo-Hyoung Bang, Ali Aldalbahi, Mostafizur Rahaman, Alexander L. Yarin, Sam S. Yoon

Bibliographic record

VenuePhysics of Fluids · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsnot available
FundersNational Institute of JusticeNational Research Foundation of KoreaKing Saud University
KeywordsMuzzleMechanicsNozzleViscoelasticityPhysicsRange (aeronautics)Supersonic speedDrop (telecommunication)Materials scienceComposite materialEngineeringMechanical engineeringBarrel (horology)Thermodynamics

Abstract

fetched live from OpenAlex

Because bloodstain patterns resulting from close-range shooting are intrinsically different from those of long-range shooting, an accurate interpretation and delineation of these phenomena are essential in forensic science and crime scene analysis. Such a delineation would be helpful, for example, to distinguish whether a suicide or a homicide had happened. If the shooting was from a long-range (most likely a homicide), muzzle gases would not be able to influence blood spatter ejected from a victim. However, in the case of a close-range shooting, muzzle gases would greatly influence blood spatter. Herein, the effect of the muzzle gases on bloodstain patterns is studied. A de Laval nozzle is used to mimic an issue of supersonic “muzzle” gas from a gun barrel. The supersonic gas flow passes through a cylinder containing defibrinated sheep blood, which is blown off and atomized into numerous drops. These drops fly away and settle onto the floor or onto vertical walls at various distances from the cylinder exit. Viscoelasticity of the defibrinated sheep blood is enhanced by adding Xanthan to model different states of blood corresponding to different conditions. An impact of a vertically-released single drop onto an inclined substrate was also studied to elucidate splashing regimes/criteria of the blood drops of different viscoelasticity levels. We found that stronger elastic forces facilitated formation of bloodstains with higher ellipticity at a higher impact angles.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.539

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)0.0000.000

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 head0.006
GPT teacher head0.227
Teacher spread0.222 · 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 designBench or experimental
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

Citations3
Published2024
Admission routes1
Has abstractyes

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