Passing the Disadvantageous Terrain and Containing Towns by Towns: Mongolian Strategy to Break Through Song’s Mountainous Defense System
Bibliographic record
Abstract
After Mongolian troops entered Sichuan Area in the Ogedei Period, although they caused serious damages to the areas of Chongqing-Sichuan in the years 1236-1241, they could not reach a substantial occupation of Sichuan due to the fault of simplex attacking-and-raiding strategy. In such a case, Southern Song troops were able to apply the policy of consolidating Sichuan.Song troops in Sichuan led by Yu Jie gradually regained lost territories and constructed a mountainous town defense system (MTDS) centering on Chonqing which has advantageous terrain of mountains. In the Mongka Period, in view of faults of military tactics in the Ogedei Period, Mianzhou Town and Lizhou Town were restored successively outside Sichuan to facilitate access to Sichuan from the north. In the years from 1258 to 1259, while the main force of Mongolian troops led by Mongka was attacking Sichuan, some soldiers were quartered to guard the occupied Song towns. Although the death of Mengge made the Song-Mongolia War come to an end temporarily, the Mongolian troops still substantially occupied certain areas of Sichuan in the Mongka Period. To the Kublai Period, on the one hand, civil strives broke out due to the fight for the title of Khan; on the other hand, strategic focus of attacking was transferred to the area of Lianghuai after the civil strives were put down. Under this circumstance, in order to contain the Song troops with partial force, the Mongolia troops in Sichuan battlefield inherited and developed the tactics of conducting military activities relying on military towns derived from the Mongka Period. Numerous military towns were restored and built against the mountainous defense system and the consolidation of occupied territories from the Mongka Period was thus completed.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".