NEW CRITERIA FOR THE ARCHAEOLOGICAL IDENTIFICATION OF BONE GREASE PROCESSING
Bibliographic record
Abstract
Bone grease processing is frequently used in archaeology to investigate human diet breadth because it constitutes a costly mode of lipid procurement. However, problems of equifinality often complicate the identification of this activity. This paper develops new criteria focused on morphology, the presence of micro-inclusions, and forms of damage that were derived from a bone grease rendering experiment that involved red deer(Cervus elaphus)long bones. Because they are poorly represented in a distinct experiment focused exclusively on marrow extraction, the criteria presented here appear to provide robust signatures of bone grease processing. A survey of the actualistic literature shows that certain bone processing activities, such as stewing and soup making, mostly involve coarse spongy fragments. Because these fragments are too large to be ingested, grease can be extracted from them only through heating or boiling. In contrast, bones ingested in bone meal or as flour necessitate pulverization. A high percentage of coarse fragments may therefore provide a proxy for cooking technology or, minimally, the use of fire, if other patterns are consistent with grease extraction. Given the evolutionary significance of these innovations, the criteria presented here may help to strengthen arguments about dietary shifts during the Paleolithic and later periods.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".