Bibliographic record
Abstract
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 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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".