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Sample Preparation for the Analysis of Water Soluble Accelerants in Fire Debris

2000· article· en· W2316925865 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 · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsnot available
FundersOhio State University
KeywordsFlammable liquidChromatographySample preparationExtraction (chemistry)DistillationSolid-phase microextractionAcetoneEnvironmental scienceDebrisMaterials scienceChemistryGas chromatography–mass spectrometryMass spectrometryOrganic chemistry

Abstract

fetched live from OpenAlex

Information on the chemical analysis of water soluble accelerants such as ethanol and acetone is sparse. It appears that only a few laboratories routinely assess fire debris samples for such ignitable liquids. We have evaluated the currently available analytical methodology for these types of compounds. Direct heated headspace, passive headspace enrichment by Diffusive Flammable Liquid Extraction (DFLEX®) and adsorption onto a Carboxen® based Solid Phase Micro Extraction (SPME) fiber are suitable methods. A newly developed micro-distillation procedure used in conjunction with SPME methodology appears to be particularly simple and promising.

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)0.005
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 score0.811
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.343
Teacher spread0.303 · 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