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Record W2174678855 · doi:10.1111/1556-4029.12975

An Impact Velocity Device Design for Blood Spatter Pattern Generation with Considerations for High‐Speed Video Analysis,

2015· article· en· W2174678855 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Forensic Sciences · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsTrent University
FundersTrent University
KeywordsSpring (device)Range (aeronautics)Computer scienceCompression (physics)SimulationMaterials scienceMechanical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

A mechanical device that uses gravitational and spring compression forces to create spatter patterns of known impact velocities is presented and discussed. The custom-made device uses either two or four springs (k1 = 267.8 N/m, k2 = 535.5 N/m) in parallel to create seventeen reproducible impact velocities between 2.1 and 4.0 m/s. The impactor is held at several known spring extensions using an electromagnet. Trigger inputs to the high-speed video camera allow the user to control the magnet's release while capturing video footage simultaneously. A polycarbonate base is used to allow for simultaneous monitoring of the side and bottom views of the impact event. Twenty-four patterns were created across the impact velocity range and analyzed using HemoSpat. Area of origin estimations fell within an acceptable range (ΔXav = -5.5 ± 1.9 cm, ΔYav = -2.6 ± 2.8 cm, ΔZav = +5.5 ± 3.8 cm), supporting distribution analysis for the use in research or bloodstain pattern training. This work provides a framework for those interested in developing a robust impact device.

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.

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)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.254
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.192
GPT teacher head0.415
Teacher spread0.223 · 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