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Record W2621092612 · doi:10.1111/1556-4029.13363

Tape Lift Sampling of Chemical Threat Agents

2017· article· en· W2621092612 on OpenAlexaff
Krista Brady, Becky Stilley, Maria Olds, James M. Egan, Evan Durnal

Bibliographic record

VenueJournal of Forensic Sciences · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsShared Health
Fundersnot available
KeywordsTileLift (data mining)Materials scienceCartridgeForensic engineeringComputer scienceComposite materialEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

Commercial-off-the-shelf (COTS) materials were evaluated as surface samplers for the Department of Homeland Security Chemical Forensics Program. The program helps evidence collectors identify trace chemical residues at incident scenes. COTS items are widely available, produced in large lots, and with strict controls. Chemical attribution signatures were collected from common surfaces. Eight tape lift candidates were considered, five were chosen based on performance and tested for analytical interferences and extraction efficiencies with 14 chemicals. Three products (duct tape, print lifters, command strips) were evaluated for uptake from common interior surfaces (glass, tile, ABS plastic). Duct tape provided highest recoveries across all surfaces. Even the most volatile analytes were detected in the ABS plastic samples (nondetections in others), regardless of tape lift material used. The porous plastic substrate provides better target retention than glass and tile surfaces. Forensic field operators should sample surfaces made of ABS plastic (keyboards, remotes, phones, etc.) whenever possible.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.062
GPT teacher head0.323
Teacher spread0.261 · 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 designBench or experimental
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

Citations1
Published2017
Admission routes1
Has abstractyes

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