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Record W3185118841 · doi:10.5744/fa.2020.4023

Laws of Taphonomic Relative Timing and Their Application to Forensic Contexts

2021· article· en· W3185118841 on OpenAlexaff
James T. Pokines

Bibliographic record

VenueForensic Anthropology · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsTaphonomyEcological successionPaleontologyStratigraphySet (abstract data type)GeologySuperposition principleArchaeologyEcologyHistoryBiologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

ABSTRACT In the analysis of taphonomic effects that have occurred to osseous remains, it is often necessary to interpret multiple overlapping changes. Individual taphonomic effects can be isolated from each other, and these follow rules of relative timing, whereby earlier or later effects can be determined. These rules are similar to those of archaeological and geological stratigraphy, from which the basic concepts of superposition and other physical relationships are derived. Taphonomic effects can be caused by multiple processes associated with early phases (death, decomposition, and scavenging of fresh remains) or with later phases (staining of bone surfaces, breakdown of the bone, and scavenging upon dry remains). The relative sequencing of the taphonomic effects to a set of remains can be used to reconstruct their postmortem history and to separate human activity, including trauma, from scavengers and other biological agencies. The four laws presented here pertain to (1) superposition, (2) positional aspect continuity, (3) original continuity, and (4) succession of changes. These laws can be applied more broadly in some archaeological/paleoanthropological situations, but the specific examples used to illustrate them here come from forensic settings.

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 score0.740
Threshold uncertainty score1.000

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 studies0.0010.031
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.266
Teacher spread0.244 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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