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Record W167135184 · doi:10.1520/jfs15150j

Forensic Textile Fiber Examination Across the USA and Europe

2001· article· en· W167135184 on OpenAlexaboutno aff
K.G. Wiggins

Bibliographic record

VenueJournal of Forensic Sciences · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Work (physics)MedicineForensic engineeringEngineering

Abstract

fetched live from OpenAlex

Crime laboratories in the USA, who undertake fiber examinations, together with members of the European Fibres Group (plus representatives from Israel, Japan, Canada, and Australia) were surveyed in 1994 and 1995, respectively, and asked to provide subject-specific information relating to personnel, equipment, training, quality control, and techniques available. The information obtained showed that generally more fiber casework is carried out in Europe than in the USA. Most laboratories are quite well equipped but those in Europe seem to be able to obtain more state-of-the-art instrumentation. Proficiency testing and peer review is accepted practice worldwide. Americans appear to update fiber collections on a more regular basis than Europeans but both keep literature up to date. Contamination is a major issue, as with all areas of trace evidence. The results from the survey suggest that minimum standards are clearly not always being observed. Careful consideration also needs to be given as to whether legitimate contact could have occurred prior to an offense being committed. The standard of forensic fiber examination worldwide is generally high. With laboratory management continuing to support the work of the Scientific Working Group for Materials and the European Fibres Group and by instigating "best practice" as set out in their guidelines, standards should continue to improve.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.328
Teacher spread0.298 · 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 designOther design
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

Citations15
Published2001
Admission routes1
Has abstractyes

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