Bringing the desert to the Nile: Some thoughts on a Predynastic terracotta model
Bibliographic record
Abstract
This article draws attention to an artefact in the Royal Ontario Museum in Canada, that has thus far received little attention despite having been in their archive for nearly a century. The object (ROM 900.2.45) is a heavily reconstructed ter-racotta rectangular-based model with openings to one side and on top. Painted and incised figures decorate the sides of the object. This paper presents a fresh approach regarding this particular artefact by addressing three issues that are intended to provide an insight into the symbolism of desert and Nile hunting. Firstly, I discuss its identification as a “house”, proposing instead that it may well resemble a Predynastic boat cabin. Secondly, I discuss the iconographic interpre-tation, showing that the scene painted in its “narrow end A” is not a depiction of goats feeding their young, but rather a hunting scene. While this claim has been suggested by other Egyptologists as well, it is still possible to bring some new ideas to the discussion. Finally, I compare these findings with the available evidence and research conducted in the last few years to test this approach and propose an alternative interpretation to both the terracotta model and its painted scenes. This object can provide more information about a much-discussed topic : the apparent contradiction between desert hunting and river navigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".