Survivability versus rate of recovery for skeletal elements in forensic anthropology
Bibliographic record
Abstract
Survivability, the ability of a skeletal element to withstand taphonomic processes, is often equated to recoverability, the probability that an element will be recovered in a forensic context, and further misused to infer the likelihood that a forensic anthropologist will recover a particular element at a scene. Consequently, researchers have utilized notions of survivability to infer that a skeletal element may be recovered when justifying the necessity of various research endeavors. This is problematic because the factors impacting survivability are not always applicable in a forensic context; the ability of a bone to survive taphonomic processes may not align with the likelihood of recovery. Empirical recovery rates are presented from two distinct contexts, with data derived from the Forensic Anthropology Data Bank based on cases performed by the late J. Lawrence Angel (1914-1986) and cases done by the University of Tennessee Knoxville (UTK). Recovery rates may be influenced by factors beyond survivability, though we do not investigate the many considerations that might explain recovery rate variation between datasets. Rather, these data exemplify the conceptual differences between notions of survivability and rates of recovery in actual casework scenarios. Thus, it is proposed that researchers consider documented rates of recovery when providing rationale for forensic anthropology research endeavors, in addition to citing a rationale that is based on inferences of survivability. This ensures that the theoretical framework of future forensic anthropology research stems, primarily, from the premise of practical application.
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 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 | 0.059 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".