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Preparation of Pyrolysis Reference Samples: Evaluation of a Standard Method Using a Tube Furnace

2012· article· en· W1524917417 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 · 2012
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsRoyal Canadian Mounted Police
Fundersnot available
KeywordsTube (container)PyrolysisTube furnaceMaterials scienceWaste managementEngineeringChemical engineering

Abstract

fetched live from OpenAlex

A new, simple method for the reproducible creation of pyrolysis products from different materials that may be found at a fire scene is described. A temperature programmable steady-state tube furnace was used to generate pyrolysis products from different substrates, including softwoods, paper, vinyl sheet flooring, and carpet. The temperature profile of the tube furnace was characterized, and the suitability of the method to reproducibly create pyrolysates similar to those found in real fire debris was assessed. The use of this method to create proficiency tests to realistically test an examiner's ability to interpret complex gas chromatograph-mass spectrometric fire debris data, and to create a library of pyrolsates generated from materials commonly found at a fire scene, is demonstrated.

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)0.007
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
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 science0.0000.000
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 head0.130
GPT teacher head0.427
Teacher spread0.297 · 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