A University Professor, and Establishing New Institutes
Bibliographic record
Abstract
Abstract When Herwig arrived in Erlangen in 1956, it was a town of some 60,000 inhabitants, dominated by the university, and the industrial powerhouse, Siemens. Historically, the town had been a seat of nobility, and the Margrave’s castle is still a dominant feature. In 1685, when Louis XIV revoked the Edict of Nantes, the Margrave gave refuge to Huguenots fleeing France, even going so far as to build an entire new quarter, Erlangen Neustadt, to house them. It set the town on a course of growth. Another development that shaped the modern-day town came the following century, with the establishment of the university in 1743. Today, housed in a new modern campus, the Friedrich-Alexander University bears the names of the Margrave who established it, and another who later expanded it, cementing its place in the fabric of the town. Many famous scientists have worked at the university including Georg Simon Ohm, whose name became the unit for electrical resistance, and mathematician Emmy Noether, whose eponymous theorem links symmetry to conservation laws—a tenet that underpins much of modern physics.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.015 |
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".