Early Iranian Riders and Cavarly
Bibliographic record
Abstract
The expansion of the Iranian peoples in first centuries of the 1st millennium BCE coincides with the creation and further development of the cavalry warfare in western Eurasia, as well as with the creation of the pastoral nomadic life-style which dominated the Great Steppe for millennia to come. The mounted warriors replaced the light chariots which dominated the Bronze Age battlefields which required perfect horsemanship however application of the recurved, double reflex. composite bow for mounted combat seemed another important factor in development of the cavalry force. Mounted archery which doubled the fire power of the mobile troops, earlier dominated by the chariots triggered the evolution of the various forms of cavalry, both as a response to a threat of the horse archers and independent forces used by the sedentary societies. Iranian contribution in spreading (and most likely invention) of the new technology is undeniable. Although horse riding and recurved composite bows were known earlier they could not overcome the power of the chariot force separately. Only the combination of the factors allowed fielding large and efficient cavalry troops as was practiced by the Scythians and became the success factor for the Achaemenid Empire. Survival of the chariots as late as the Seleucid times was possible because of changing their tactical function from the highly mobile shooting platform to heavy, at least partially, armored terror and shock weapon.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".