Bibliographic record
Abstract
This article examines Sassanian siege warfare and technology in the domains of ballistae, ‘scorpions’, catapults, and battering rams. Sassanian siege warfare necessitated the use of protection/shielding for personnel (combat troops, engineers, laborers), mounds, mining, scaling of walls, as well as the digging of ditches and trenches. Archery barrages played a seminal role in support of siege operations. The arteshtārān (lit. warriors; mainly paighan infantry, archers, and savārān cavalry) and pil-savār (elephant warriors/riders) would undertake combat operations with manual labour provided by peasant recruits. Battle elephants could also be used in siege operations (for example at Nisibis, 350 CE). The environmental element of water was utilised (for example during the sieges of Nisibis, 337 or 338 CE and 350 CE). Incendiary factors could also be weaponised in siege operations. In summary, Sassanian siege warfare capabilities appear to have achieved proficiency levels equivalent to contemporary Roman armies.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.007 | 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".