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Smoking for the Führer

2002· book-chapter· en· W4388381242 on OpenAlexaboutno aff
Walter Gratzer

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicHistorical Medical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsNazismGermanCommunismQuarter (Canadian coin)PersecutionArt historyHistoryLawPolitical scienceArtPoliticsArchaeology

Abstract

fetched live from OpenAlex

Abstract Fritz Houtermans’s career was the stuff of fiction. He was German by birth but grew up and studied in Vienna. He was a physicist with, according to his friend Otto Frisch [20], a profound understanding of quantum theory. He pursued his theoretical work in Viennese cafes, where his prodigious capacity for coffee became legendary. His growing reputation took him to Germany, to one of the great centres of theoretical physics in Gottingen. Houtermans was one-quarter Jewish, so that, although he was proud of his ancestry’—’when your ancestors were still living in the trees’, he would tell his Aryan colleagues, ‘mine were already forging cheques’—he was not under threat of racial persecution by the Nazis. He was, however, a committed Communist and for many years a party member, and this would have put his life in danger. He therefore decamped to England, where he worked at the EMI laboratories, and all but discovered the laser (the means, first achieved in 1960, of generating light of a single wavelength with a very high intensity). But life in England was not to his taste and he complained especially’ about the smell of boiled mutton. He moved again, this time to fulfil his old ambition of working in the Soviet Union. He found employment in the Physico-Technical Institute in Kharkov, which then housed a brilliant cluster of physicists, among them the great Lev Landau 1137].

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.518
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.099
GPT teacher head0.336
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2002
Admission routes1
Has abstractyes

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