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Record W2028746330 · doi:10.1111/1556-4029.12305

Effect of Substrate Interferences from High‐Density Polyethylene on Association of Simulated Ignitable Liquid Residues with the Corresponding Liquid

2013· article· en· W2028746330 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 · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicForensic Fingerprint Detection Methods
Canadian institutionsSmiths Detection (Canada)
FundersMicrosoft
KeywordsHigh-density polyethyleneChromatographyGasolinePolyethyleneMaterials scienceMass spectrometrySubstrate (aquarium)Analytical Chemistry (journal)Residue (chemistry)Liquid fuelChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of substrate interferences from high-density polyethylene (HDPE) on the ability to associate an ignitable liquid residue with the corresponding liquid standard, using statistical procedures, is demonstrated. Gasoline, kerosene, and lighter fluid, at three different evaporation levels, were spiked onto HDPE and subsequently burned to generate simulated ignitable liquid residues (ILRs). Samples were extracted using a passive headspace procedure and analyzed by gas chromatography-mass spectrometry. The total ion chromatograms were subjected to data pretreatment procedures prior to principal components analysis and Pearson product moment correlation. Using the combination of these statistical procedures, simulated ILRs were successfully associated with the corresponding liquid type, despite the presence of compounds inherent to the HDPE substrate, as well as those resulting from pyrolysis of the substrate.

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)0.003
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 score0.349
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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