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Record W2088212983 · doi:10.1002/cplu.201402003

Reading Cadaveric Decomposition Chemistry with a New Pair of Glasses

2014· article· en· W2088212983 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChemPlusChem · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of Ontario Institute of Technology
KeywordsDecompositionChemistryMass spectrometryGas chromatographyChemical decompositionThermal decompositionDesorptionEnvironmental chemistryAnalytical Chemistry (journal)ChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The chemical processes of human cadaver decomposition are complex and not well understood. The study of decomposition chemistry aims to elucidate the postmortem processes, particularly relating to the production of volatile organic compounds (VOCs) throughout the various decomposition stages. The use of thermal desorption coupled with comprehensive two‐dimensional gas chromatography time‐of‐flight mass spectrometry (TD‐GC×GC‐TOFMS) has allowed for the VOC profile of decomposition odor above pig carcasses (human analogues) to be determined. An enhanced data‐processing approach combining Fisher ratio calculations with principal component analysis assisted in the identification of the major classes of compounds that contribute to the VOC profile and their variation across decomposition stages. Detection and profiling of these VOCs is valuable for understanding the mechanisms by which human‐remains detection (HRD) dogs locate victims in mass disasters and forensic investigations.

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)0.000
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 score0.023
Threshold uncertainty score0.193

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 science0.0000.000
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 head0.011
GPT teacher head0.217
Teacher spread0.206 · 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