Bibliographic record
Abstract
The Kangxi Emperor employed Jesuit brothers (1708–18) to produce maps of the provinces of China using a combination of Western and Chinese survey methods. The maps were completed by 1721. They were sent back to Europe and became the basis for maps of China produced by Jean Baptiste Bourguignon d'Anville in 1735. The main changes from traditional Chinese mapping were to use latitude and longitude as primary coordinates, map them using a spherical projection, and use astronomical measurements of latitude and longitude to establish baselines. Changes in latitude and longitude were found using traditional metric survey and relationships between distance north–south and latitude and distance east–west and longitude to convert to degrees. A digitized facsimile of the 1721 map series is available from the US Library of Congress. In this article, the digitized images were used to reconstruct the parameters of the sinusoidal projection, establish scale, and re-project and mosaic the maps into various forms for presentation. The accuracy of five of the province maps is discussed in detail. It is found that poor astronomical measurements of longitude are the most serious issue for the maps, but that apart from areas distorted by the poor longitude estimates, the accuracy was commensurate with that of European land maps of the time.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".