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Providing Evidence Based Opinions On Time Since Intercourse (TSI) Based On Body Fluid Testing Results Of Internal Samples

2011· article· en· W2322910049 on OpenAlexaffvenue
Renata Dziak, Linda Parker, Vanessa Collins, Sarah Johnston

Bibliographic record

VenueCanadian Society of Forensic Science Journal · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsScientific evidenceSampling (signal processing)Scientific literaturePsychologyMedicineBiologyComputer scienceStatistics

Abstract

fetched live from OpenAlex

A critical evaluation of the current available literature on Time Since Intercourse (TSI) was performed at the Centre of Forensic Sciences (CFS) to determine whether there is scientific support for reliable, evidence based opinions on TSI. The assessment included a review of published scientific literature and internal studies focusing on the persistence of spermatozoa, prostatic acid phosphatase and prostate specific antigen (PSA/p30) from internal samples of living individuals. From this review, it was concluded that, despite variation in sampling methodologies, there is scientific support for the development of TSI estimate guidelines based on the serological testing results from internal swabs and smears.

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)0.145
metaresearch head score (Gemma)0.359
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: none
Teacher disagreement score0.145
Threshold uncertainty score0.767

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1450.359
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0120.006
Science and technology studies0.0020.004
Scholarly communication0.0050.007
Open science0.0040.004
Research integrity0.0050.005
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 head0.073
GPT teacher head0.306
Teacher spread0.233 · 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

Citations17
Published2011
Admission routes2
Has abstractyes

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