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Lifting Fingerprints from Skin Using Silicone

2009· article· en· W2322288753 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSiliconeFingerprint (computing)Lift (data mining)Materials scienceComputer scienceBiomedical engineeringPattern recognition (psychology)Artificial intelligenceComposite materialData miningEngineering

Abstract

fetched live from OpenAlex

There have been various methods tested to lift fingerprints from skin; however a sure method has yet to be found. Using magnetic powder to enhance a fingerprint and then lifting with silicone has produced a few successes and many failures, similar to other techniques such as iodine/silver plate transfer. Progress needs to be made in lifting fingerprints from skin; therefore, it is important to develop a new method. The silicone method was tested using variables such as time, temperature, and different silicone brands. The findings indicate that the enhancement of fingerprints on skin showed the best results using magnetic powder, that it is possible to get fingerprints after up to 43 hours, and that it is possible to locate a fingerprint at body surface temperatures between 21°C and 48°C

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0030.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.334
Teacher spread0.296 · 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