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Record W4386541162 · doi:10.1080/00085030.2023.2255410

A preliminary study evaluating the relationship between force and incised trauma on pig rib bones

2023· article· en· W4386541162 on OpenAlexafffundvenue
Cathy Ngọc Hân Tran, Eugene Liscio

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsUniversity of TorontoSimon Fraser University
FundersUniversity of Toronto Mississauga
KeywordsPerpendicularBlade (archaeology)Rib cageGeologyAnatomyMaterials scienceOrthodonticsMathematicsStructural engineeringGeometryEngineeringBiologyMedicine

Abstract

fetched live from OpenAlex

In forensic contexts, understanding of the complicated relationships between level of force, type of knife blade, and dimensions of incisions remains limited. The purpose of this research was to explore how incisions on pig rib bones vary depending on the type of knife blade and quantity of perpendicular force inflicted. A cutting rig (designed to position a bone, knife, and weights) facilitated the creation of incisions on fleshed rib bones (n = 59), defleshed rib bones (n = 77), and synthetic materials (n = 36). Five different masses of weights (measuring 4381, 8861, 13515, 18267, and 23343 g) were applied to four knife blades (two serrated and two non-serrated blades), with each combination of variables repeated thrice. 3D digital microscopy was utilized to model and measure each incision. The two-way ANOVA found significant differences in depth and length between knife types and levels of force across all sample sets (p < 0.05), with greater perpendicular forces and serrated blades creating longer and deeper cuts. These findings demonstrate that there is a complex relationship between force, type of knife blade, and the related dimensions of incisions.

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)0.003
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.096
GPT teacher head0.375
Teacher spread0.279 · 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 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
Published2023
Admission routes3
Has abstractyes

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