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Use of a Solid Absorbent and an Accelerant Detection Canine for the Detection of Ignitable Liquids Burned in a Structure Fire

2007· article· en· W2160861207 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

VenueJournal of Forensic Sciences · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsRoyal Canadian Mounted PoliceUniversity of Fredericton
Fundersnot available
KeywordsGasolineWaste managementEnvironmental sciencePoison controlResidue (chemistry)Gas chromatography–mass spectrometryChromatographyMass spectrometryMaterials scienceChemistryEngineeringOrganic chemistryMedicineMedical emergency

Abstract

fetched live from OpenAlex

Ignitable Liquid Absorbent (ILA), a commercial solid absorbent intended to assist fire scene investigators in sample location and collection, has been field tested in three separate room fires. The ability of the ILA to detect and absorb different amounts of gasoline, odorless paint thinner, and camp fuel on two different substrates after a full-scale burn was assessed against results from an accelerant detection canine and laboratory analysis using gas chromatography-mass spectrometry (GC-MS). The canine correctly alerted on most of the panels that contained an ignitable liquid after the fire, while the ILA indicator dye failed to indicate in the presence of gasoline and camp fuel. GC-MS results for ignitable liquid residue from each panel and from the ILA showed that ILA absorbed odorless paint thinner and camp fuel from most of the test panels, but failed to absorb gasoline from the panels on which gasoline was confirmed to be present.

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)0.006
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.071
GPT teacher head0.382
Teacher spread0.311 · 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