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Record W2803128997 · doi:10.1080/00085030.2018.1463274

The use of a forensic blood substitute for impact pattern area of origin estimation via three trajectory analysis programs

2018· article· en· W2803128997 on OpenAlexafffundvenue
Sumiko Polacco, Mike Illes, Theresa Stotesbury

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsTrent University
FundersTrent University
KeywordsEstimationForensic scienceStatisticsTrajectoryMathematicsPattern analysisSignificant differenceComputer scienceGeographyArtificial intelligenceArchaeologyEngineeringPhysics

Abstract

fetched live from OpenAlex

This study explores the use of forensic synthetic blood substitute (FBS) for impact pattern simulation and area of origin estimation. Ten impact patterns were created at a known origin using the FBS and were analyzed by groups of undergraduate students participating in a basic bloodstain pattern analysis course. The students selected 20 upward-moving stains from their given patterns to estimate an area of origin. Three linear trajectory models – BackTrack™, Hemospat, and Sherlock – were used to estimate each pattern's area of origin. Coordinate data from each model's analysis were compared with the known origin and between programs at the x-, y-, z-coordinates, and overall. Results from this analysis yielded estimates comparable to the known. A one-way ANOVA found no significant difference between programs at the x- (p = 0.79), y- (p = 0.84), z- (p = 0.96) coordinates, and overall (p = 0.81). These results support the practical use of the FBS as an alternative to whole blood for impact pattern simulations and area of origin estimation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.004
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 head0.046
GPT teacher head0.310
Teacher spread0.264 · 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.

Study designBench or experimental
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

Citations5
Published2018
Admission routes3
Has abstractyes

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