Palaeodietary reconstruction of wild and domestic goats using dental microwear texture analysis. A case study from two early Neolithic sites in the southern Levant
Bibliographic record
Abstract
Dental microwear (DMA) is a tool used for the palaeodiet reconstruction of animals in Archaeology. The use of this proxy on domestic ungulates provides valuable information to reconstruct livestock strategies, yet it presents several methodological limitations. Most studies have been carried out using low-magnification DMA and the interpretations often relied on comparisons with databases of extant wild ungulates. In addition, several studies have highlighted challenges in discerning diets in extant domestic caprines. In parallel, dental microwear texture analysis (DMTA) – a quantitative methodology based on 3D micro-texture height maps – has shown better discrimination. In this paper, we explore the capacity to distinguish four different management strategies of domestic goats (Capra hircus) and three species of wild ibexes (Capra nubiana, C. pyrenaica and C. ibex) using DMTA. Results revealed good discrimination among extant domestic goat populations and between wild and domestic goats. This new dataset was subsequently used to characterise the palaeodiet of archaeological goats from two Pre-Pottery Neolithic B sites in the southern Levant. Preliminary findings suggest evidence of human intervention in goats at least during the early 8th millennium BCE. In addition, incorporating various current management strategies has enhanced our understanding of early goat domestication in southern Levant.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".