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Record W4296598369 · doi:10.52214/uw.v30i.9320

Sweet Water on the Sea Route to China

2022· article· en· W4296598369 on OpenAlexfundno aff
Elizabeth Lambourn

Bibliographic record

VenueAl-ʿUsur al-Wusta · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMaritime and Coastal Archaeology
Canadian institutionsnot available
FundersYork University
KeywordsChinaAllotmentWater transportCivil engineeringNinthInternational watersEngineeringGeographyArchaeologyPolitical scienceEnvironmental engineeringLawEcologyWater flow

Abstract

fetched live from OpenAlex

Potable, or “sweet,” water was the foundation stone of maritime provisioning and, by implication, route planning on all but the shortest voyages in the premodern world. Without it, maritime trade and all other forms of seaborne exchange and circulation were effectively impossible. Yet water sources and technologies of transportation have been comparatively neglected in Indian Ocean history and archaeology. This paper rereads data from the ninth-century section of the Akhbār al-Ṣīn wa-l-Hind (Accounts of China and India) alongside recent evidence from two contemporary shipwrecks to examine the spacing of watering stops and the technologies of water transportation employed on long-distance sailings between the Gulf and Chinese ports. Working from the 2021 publication of the volumetric capacity of a group of so-called torpedo jars excavated in Thailand, this article proposes some preliminary quantitative estimates of the volume of freshwater, and thus the number of water jars, required on board vessels at the time. In so doing it raises important questions about the portability and handling of torpedo jars as well as the varied uses and reuses of such transport jars. Weaving passages from the Akhbār with information on ceramic remains from the Phanom Surin and Belitung wrecks, this article aims to start a conversation about the very real physical and physiological parameters that underlay Indian Ocean connectivity and the water transportation technologies that underpinned them.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0440.001

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.

Opus teacher head0.018
GPT teacher head0.210
Teacher spread0.192 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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