Coins, Weights and Measures in the Arabian Gulf during the European Commercial Activity Period 1600-1800
Bibliographic record
Abstract
This study examines the coins, weights and measures used in the Gulf region during the seventeenth and eighteenth centuries, as the region attracted traders from different nationalities during this period: Portuguese, English, Dutch and Indians. Some local people and people from the surrounding areas, such as Arabs, Persians, Turks and others practiced commercial activities in the region. Of course, all of them were paying for the goods they buy, which made the region teeming with different types of coins. That, in turn, made it somewhat complicated for researchers to find out the exact value of those coins, as well as to determine the time periods during which those currencies came to the region or for how long they were in use in commercial transactions. Despite the fact that the weights and measures that had been used in the region during the study period were varied, changing over time, and influenced-positively or negatively-by the surrounding states and countries, but we can talk more confidently thereon than currencies which were exchanged in the region.It's worth mentioning herein; that many resources, references and reports, that have been referred to herein state many currencies and measures which had not necessarily been used although it was existed, also there were currencies, measurements and weights that were not mentioned therein.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".