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A Review of Fire-Related Deaths in Alberta

2010· review· en· W2084546334 on OpenAlexaffvenueabout
Kathryn Waterhouse

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2010
Typereview
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsForensic engineeringEnvironmental scienceGeographyEngineering

Abstract

fetched live from OpenAlex

Burnt remains and fire-related research are growing areas of investigation in forensic anthropology; however, for this research to be valuable to criminal death investigations it must be comparable with real death circumstances. To ensure this relevance, an understanding of the circumstances (and victim profiles) relating to fatal fires is required. As a first step in addressing these issues, this paper examines and interprets fire deaths occurring in Alberta, Canada over a ten-year period between January 1999 and December 2008. The results show that 274 individuals died in 231 fire incidents during the period under review. The majority of these deaths occurred in residential fires (65%), vehicle fires following vehicle collisions (20%) and stationary vehicle fires (8%). Analysis also indicates that the victim and burn profile vary with fire type. In residential fires, young and elderly individuals are at risk whereas in vehicle fires following vehicle collisions, middle-aged adults are the most common victims. Victims of residential fires exhibit varying degrees of burn damage and remains may be reduced to calcined bone fragments. More severe burning is seen in trailer or cabin fires compared to house fires. In vehicle fires, burn damage is often significant, with remains frequently exhibiting thermal amputation. Fatal residential and stationary vehicle fires are more common in the winter and spring seasons, in contrast to vehicle fires following vehicle collisions which occur most often in the fall.

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.

How this classification was reachedexpand

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.956
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.037
GPT teacher head0.361
Teacher spread0.324 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2010
Admission routes3
Has abstractyes

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