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Record W2922234689 · doi:10.1080/00085030.2019.1581691

The next level aqueous electrolyte reagent (AER) for development of latent fingermarks

2019· article· en· W2922234689 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsnot available
FundersUniversity Grants Commission
KeywordsReagentPorosityMaterials scienceAqueous solutionElectrolyteChemical engineeringChemistryComposite materialOrganic chemistryElectrodePhysical chemistryEngineering

Abstract

fetched live from OpenAlex

Research literature shows that different forms of aqueous electrolytes develop latent fingermarks on metals, glass and plastic surfaces. These reports describe the development of latent fingermarks using specific electrolytes for specific surfaces. Surface dependency is still a challenge in the fingerprint development process. The current study involves a newly formulated aqueous electrolyte reagent (AER) applied on a variety of surfaces. A proposed reaction mechanism has also been studied and supported by SEM-EDS. A large variety of porous, semi-porous, and non-porous surfaces have been tested for the development of latent fingermarks. Satisfactory response of AER has been observed.

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)0.001
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 score0.917
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.065
GPT teacher head0.323
Teacher spread0.258 · 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