A Zooarchaeological Signature for Meat Storage: Re-Thinking the Drying Utility Index
Bibliographic record
Abstract
Although the practice of food storage is important to many questions addressed by archaeologists, demonstrating its presence in archaeological contexts can be difficult or impossible. One potentially useful approach to meat storage is the concept of the Drying Utility Index, introduced by Lewis Binford (1978) to predict which carcass portions, with attached bone, will be selected for storage by drying. However, this index has not been widely used by zooarchaeologists, at least in part because the calculations involved in its derivation are extremely complex. This paper presents a new, simplified index, the Meat Drying Index, which is easier to calculate and more transparent than the Drying Utility Index, yet which retains all of its key attributes. This new index is applied to caribou bone samples from two regions: Binford's (1978) Nunamiut data from northern Alaska, and the contents of three caches from the Barren Grounds of Canada, near Baker Lake, Nunavut. In both cases, the Meat Drying Index correlates with the observed element frequencies as well as, or better than, the original Drying Utility Index. As a result, the new index may prove applicable to element distributions from a wide range of archaeological contexts in which storage of meat by drying is suspected.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".