Bibliographic record
Abstract
Bone grease rendering is a low-return activity well described in the ethnohistorical and ethnographic literature. However, identifying this activity in archaeological contexts is complex because diagnostic criteria are few. The goals of this article are twofold: (1) to provide new experimental data on bone grease manufacture for assemblages associated with severe fragmentation, and (2) to assess how these data can be used to make stronger inferences about skeletal fat processing in the archaeological record. The results presented here show that, despite some variation, several forms of damage appear to be diagnostic of bone grease manufacture, regardless of the degree of fragmentation. The results indicate that extensive pounding produces many fragments that can be identified as deriving from articular ends, which conflicts with the oft-cited notion that articular ends are destroyed “beyond recognition” during this activity. Consequently, assemblages with few epiphyseal remains are not consistent with bone grease rendering, assuming that the comminuted fragments were not burned or discarded off-site after boiling. Because bone grease manufacture produces many small fragments, a close analysis of the indeterminate remains is strongly recommended, as is the use of fine mesh screens (2 mm or smaller) in excavations.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".