<scp>GIS</scp>‐enabled historiography to determine travel routes during the Western Han period via agent‐based models and least‐cost path analysis
Bibliographic record
Abstract
Abstract GIS‐enabled historiography allows us to shed light on missing or poorly understood aspects of historical events. Here, we use agent‐based models (ABMs), least cost path analysis (LCPA), and space–time paths to recreate and evaluate possible modes and routes of travel undertaken by Shi Rao, a Western Han official. A diary was found in Shi Rao's tomb that includes information about his travels over 1 year (11 BCE), including start and end times of his journeys. But it leaves out details regarding modes of transport or exact routes. Using Tan Qixiang's historical atlas, we digitized river networks and utilized modern topographic data to digitally recreate the landscape as it was during Shi Rao's travels. This was then used to evaluate possible journeys via rivers (using ABMs) and roads (using LCPA), and compared to historical speed of boats, carts and horses, with findings indicating that horseback may have been the viable option.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".