Bibliographic record
Abstract
This evaluation of Fra Mauro's world map is due to coincidence. After studying Marco Polo's route in Asia for a decade, in early 2016, I discovered Professor Piero Falchetta's important book about the subject, which every major library should have. Considering his 2,921 entries, one may assume that Falchetta has identified about 90% of Fra Mauro's place names. Our aim was to add about another 3%, including some proposed revisions. Myanmar, Thailand, Central Asia, and China offered many new cartographical fix points. Some of these are confirmed by the identifications and conclusions of the Thai scholar Thavatchai Tangsirivanich. The accuracy of the Mediterranean and the Black Sea on Fra Mauro's map invites a map-maker to trace their shorelines onto vellum and overlay it on a modern map of Eurasia in the Mercator projection. This methodology may seem too simple, but such visual comparison yields invaluable conclusions, including a table of geographical points with true latitudes and longitudes compared with those obtained from Fra Mauro. Many maps from past centuries are compared with his across South Asia from Turkey to Vietnam.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.006 |
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".