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Record W4368377618 · doi:10.1111/tgis.13056

<scp>GIS</scp>‐enabled historiography to determine travel routes during the Western Han period via agent‐based models and least‐cost path analysis

2023· article· en· W4368377618 on OpenAlexafffund
Raja Sengupta, Griet Vankeerberghen, Ruoxuan Wen, Jing Rao, Yanbing Chen

Bibliographic record

VenueTransactions in GIS · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArchaeology and ancient environmental studies
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsHistoriographyGeographyCartographyPath (computing)Period (music)Travel timeHistoryComputer scienceGenealogyOperations researchArchaeologyEngineeringTransport engineeringArt

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.129
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.207
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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