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Record W3157917199 · doi:10.5744/fa.2020.4018

Experimental Formation of Marine Abrasion on Bone and the Forensic Postmortem Submergence Interval

2021· article· en· W3157917199 on OpenAlexaff
James T. Pokines, Melissa Menschel, Savannah Mills, Elena Janowiak, Reshma Satish, Caroline D. Kincer

Bibliographic record

VenueForensic Anthropology · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsAbrasion (mechanical)TaphonomyBioerosionSedimentGeologyOdocoileusStage (stratigraphy)OceanographyMaterials sciencePaleontologyCoralComposite material

Abstract

fetched live from OpenAlex

Human skeletal remains are frequently recovered from marine environments, where they have undergone months or years of immersion, and decomposition stage is no longer a possible method from which to estimate the postmortem submergence interval (PMSI). Over these longer PMSIs, a common taphonomic alteration that forms is the wearing and rounding of surfaces from marine abrasion, caused by repeated agitation in sediment or against rocks. Little is known about the timing of these changes and how to measure or score the degree of alteration. In a laboratory setting, multiple dry, defleshed bones of white-tailed deer (Odocoileus virginianus) were agitated in saltwater for varying intervals with abrasive sediment (1, 2, 4, 6, 8, and 10 days) and with rocks (5 and 12 days) using a laboratory tumbling device. These were compared to human remains cases from the Office of the Chief Medical Examiner, Boston, MA that had been in the Atlantic Ocean for known intervals. A five-stage (0–4) marine abrasion scoring system was devised for the taphonomic analysis of forensic cases from marine environments to allow for direct comparison among cases. A high degree of correlation between abrasion stage and known PMSI was detected.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.893
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.054
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations4
Published2021
Admission routes1
Has abstractyes

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