The Names of the Kings of Northern Mesopotamia in Cuneiform Texts (Subarto, Khamazi, Simorrum) as examples
Bibliographic record
Abstract
The kings of Northern Mesopotamia had many title which is in fact a reflection of their authority from a political, Social religious and Military aspect and from their titles we can understand the nature of their ruling weather it was religious or military or social. In this research we try to tell about the royal titles which the kings of northern Mesopotamia held, named themselves with and distinguish them in the cuneiform sources because telling about the king is confessing by his authority, telling about the king is confessing by his authority, regarding him as a secured and it is a confess by the width of his authority. The oldest title mentioned in cuneiform sources was EN Which is a religious title in origin, then ENSI which means the prince who was the governor of the city who held civil authority, then LUGAL which meant the king whose authority was bigger than ENSI and he held both religious and civil authority. The LUGAL was king of Sumer and Akkad also the king of four quarter of the world, sometimes the rulers were of military title like (šagina) which means the military leader. This research depend on many cuneiform sources and Archaeological evidences like the cuneiform sources which exist on rocky facades or cylinders seals or other Archeological evidences found during the excavations, which left by the kings of Mesopotamia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".