Chapter 7. Technical University of Munich (TUM): the Second Decade (1983–1993)
Bibliographic record
Abstract
Technical University of Munich (TUM): the Second Decade (1983–1993). In late 1985 I received the invitation to a tour to Canadian and US West Coast Universities which offered a unique chance to see some top research institutions on a perfectly organized schedule. The itinerary of the PWCIL started in Canada at the University of Alberta in Edmonton, followed by the University of British Columbia (UBC) and Simon Fraser University (SFU) in Vancouver and the University of Victoria on Victoria Island. My visit at SFU was particularly important because I met with Daniel B. Leznoff who led a potent group active in gold chemistry, being particularly successful regarding the solid-state phenomena of dicyanoaurates(I). My final visit of the Canadian part of my tour on Victoria Island was very eventful. My host at the University of Victoria was Stephen Stobart, who was then engaged in studies of transition metal silicon and germanium compounds including also metal siloxanes. Stephen was a very generous host and after my lectures and discussions with his group he showed me the beauty of the island and had me as a guest in his yacht club. I had known very little about the ethnical background of the “first nation” population of this coast with its central pacific origin and was impressed by the exhibits in the museums and parks both in Vancouver and even more so in Victoria. …
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.093 |
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".