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Record W2327757288 · doi:10.1093/jat/bku053

The Influence of Body Position and Microclimate on Ketamine and Metabolite Distribution in Decomposed Skeletal Remains

2014· article· en· W2327757288 on OpenAlexaff
H. M. Cornthwaite, James H. Watterson

Bibliographic record

VenueJournal of Analytical Toxicology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForensic Entomology and Diptera Studies
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMicroclimateMetaboliteChemistrySupine positionInternal medicineBiologyMedicineEcologyBiochemistry

Abstract

fetched live from OpenAlex

The influence of body position and microclimate on ketamine (KET) and metabolite distribution in decomposed bone tissue was examined. Rats received 75 mg/kg (i.p.) KET (n = 30) or remained drug-free (controls, n = 4). Following euthanasia, rats were divided into two groups and placed outdoors to decompose in one of the three positions: supine (SUP), prone (PRO) or upright (UPR). One group decomposed in a shaded, wooded microclimate (Site 1) while the other decomposed in an exposed sunlit microclimate with gravel substrate (Site 2), roughly 500 m from Site 1. Following decomposition, bones (lumbar vertebrae, thoracic vertebra, cervical vertebrae, rib, pelvis, femora, tibiae, humeri and scapulae) were collected and sorted for analysis. Clean, ground bones underwent microwave-assisted extraction using acetone : hexane mixture (1 : 1, v/v), followed by solid-phase extraction and analysis using GC-MS. Drug levels, expressed as mass normalized response ratios, were compared across all bone types between body position and microclimates. Bone type was a main effect (P < 0.05) for drug level and drug/metabolite level ratio for all body positions and microclimates examined. Microclimate and body position significantly influenced observed drug levels: higher levels were observed in carcasses decomposing in direct sunlight, where reduced entomological activity led to slowed decomposition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.116

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations14
Published2014
Admission routes1
Has abstractyes

Explore more

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