Laws of Taphonomic Relative Timing and Their Application to Forensic Contexts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".