From a Small Plot in Knoxville to a Worldwide Footprint
Bibliographic record
Abstract
The first outdoor human decomposition research facility was established by Dr. William Bass in 1980 at the University of Tennessee in Knoxville. Initial research at the Anthropology Research Facility (ARF) examined some of the large-scale environmental factors that contribute to decomposition and time since death estimates. As taphonomic research grew into a holistic and interdisciplinary field, the importance of macro-and microenvironmental factors became clear, and additional facilities opened in different areas of the globe. Research conducted at outdoor decomposition facilities now investigates complex relationships between the decomposing body and its environment in diverse landscapes across the world. These facilities play an important role in forensic science by providing real-world laboratory environments, research material, opportunities for research, and documented modern skeletal collections. In addition, they provide opportunities for training both professionals and students in many fields that require human remains, including human remains recovery, death investigation, and cadaver dog training. In the United States today, the resulting ethically donated human skeletal collections have increasing importance in understanding the changes in modern human bodies. This article examines the growth and function of what have been colloquially referred to as “body farms” over the past four decades.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.004 |
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".