Bibliographic record
Abstract
Who discovered America? By some accounts, it was Christopher Columbus, who left Palos, Spain, on August 3, 1492, and dropped anchor off the coast of Santo Domingo, the Dominican Republic, not far from Haiti, on October 12 of the same year. Other historians, paleographers, and polemicists offer different accounts based on literary and archeological evidence suggesting that first the Phoenicians and later the Vikings arrived on these coasts and discovered the New World well before Genoa's maritime genius. Even assuming these latter accounts are true, we cannot ignore the outstanding achievements of Columbus: he introduced Europe to the new continent and initiated a new adventure in human endeavor, what have come to be known as modern times. Nevertheless, only a small part of the New World remains associated in name with this discoverer-Columbia. Place names have been unfair in that respect, and the name America was proposed to designate a quarter of the world in honor of Amerigo Vespucci, who made several expeditions to the lands discovered by Columbus and who succeeded him as the piloto mayor. 1 But what, you are no doubt asking yourself, does this have to do with skin diseases?
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 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".