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Record W2144679788 · doi:10.1366/000370206778999085

Detection and Mapping of Latent Fingerprints by Laser-Induced Breakdown Spectroscopy

2006· article· en· W2144679788 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.

Bibliographic record

VenueApplied Spectroscopy · 2006
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFingerprint (computing)Laser-induced breakdown spectroscopySpectroscopyLaserSubstrate (aquarium)WaferMaterials scienceRidgeAnalytical Chemistry (journal)Latent imageChemistryOpticsOptoelectronicsChromatographyArtificial intelligencePhysicsComputer scienceGeologyImage (mathematics)

Abstract

fetched live from OpenAlex

Detection of latent fingerprints on a Si wafer by laser-induced breakdown spectroscopy (LIBS) is demonstrated using approximately 120 fs pulses at 400 nm with energies of 84 +/- 7 microJ. The presence of a fingerprint ridge is found by observing the Na emission lines from the transferred skin oil. The presence of the thin layer of transferred oil was also found to be sufficient to suppress the LIBS signal from the Si substrate, giving an alternative method of mapping the latent fingerprint using the Si emission. A two-dimensional image of a latent fingerprint can be successfully collected using these techniques.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.193
Teacher spread0.188 · 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