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Record W4392613375 · doi:10.1016/j.fsir.2024.100360

Concrete evidence: Analysis of aggregate and cement in a homicide investigation

2024· article· en· W4392613375 on OpenAlexfundno aff
Alastair Ruffell, Jennifer McKinley

Bibliographic record

VenueForensic Science International Reports · 2024
Typearticle
Languageen
FieldEngineering
TopicGeophysical Methods and Applications
Canadian institutionsnot available
FundersQueen's University
KeywordsHomicideAggregate (composite)Forensic engineeringCementCriminologyEngineeringGeotechnical engineeringPsychologyPoison controlMaterials scienceHistoryArchaeologySuicide preventionComposite materialEnvironmental healthMedicine

Abstract

fetched live from OpenAlex

The unusual body deposition site described comprised three elements of concealment: i) a covert stream-based ravine some 60 m from the suspect’s home; ii) partial grave dug into the ravine bank and iii) final concealment using concrete slabs. Disaggregation and sieving of concrete samples from the site, suspect residence(s) and control samples was carried out. These allowed informative exclusion of all but one control sample and provided a range of possible comparisons that may reflect the sequence of concrete slab selection, transport and use in covering the victim. The textures/colours of disaggregated, dried sediment size fractions also proved useful in conveying principles of exclusion to the court and jury at a subsequent murder trial. This work flows from basic (visual) observation of dry, cut blocks, through regular laboratory procedures of thin section work to disaggregation and size separation of aggregate-cement fractions. Graphical presentation of each analysis provided effective communication of geological science during the trial at court, concluding with a verdict of guilty by aggravated murder.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.313
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes1
Has abstractyes

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