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Record W2014820269 · doi:10.1520/jfs2003224

Deducing Drop Size and Impact Velocity from Circular Bloodstains

2004· article· en· W2014820269 on OpenAlexaff
N. Z. Mehdizadeh, S. Chandra

Bibliographic record

VenueJournal of Forensic Sciences · 2004
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsDrop (telecommunication)Materials scienceDrop impactSurface roughnessComposite materialSurface finishOpticsMineralogyMechanicsChemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

An experimental study was done to determine the diameter and velocity of blood drops falling on a surface by measuring the size of bloodstains they produced and counting the number of radial spines projecting from them. Bloodstains were formed by releasing drops of pig blood with a range of diameters (3.0-4.3 mm) and impact velocities (2.4-4.9 m/s), onto four different flat surfaces (glass, steel, plastic, paper) with varying roughness (0.03-2.9 microm). High-speed photography was used to record drop impact dynamics. Bloodstain diameters and the number of spines formed around the rim of stains increased with impact velocity and drop diameter. Increasing surface roughness reduced stain diameter and promoted merging of spines, diminishing their number. Equations are presented that explicitly relate drop diameter and impact velocity to measurements of stain diameter and number of spines.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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 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

Citations91
Published2004
Admission routes1
Has abstractyes

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