Bibliographic record
Abstract
in the mid-1930s, one presumably capable of performing—albeit slowly—any calculation that could be performed by anyone at any time. But a bit later in the same decade he designed two rather more physical machines, ones that were by no means universal. The first was a project of his while he was in Princeton, in 1937, and was assembled from relays that he built himself in the workshop of the Princeton physics department. This was to some extent a collaboration with a Canadian graduate student in physics named Malcolm MacPhail, who wrote in a letter to Turing’s biographer, Andrew Hodges, “my small contribution to the project was to lend Turing the key to the shop, which was probably against all the regulations, and to show him how to use the lathe, drill press, (etc.) without chopping off his fingers. And so, he wound the relays; and to our surprise and delight the calculator worked. ” The machine apparently did only one task—it multiplied two integers in binary format, which at that time, when adding machines all used decimal format, would have been an innovation. The second machine was the one whose blueprint is on the cover and the preceding pages. Its purpose was to assist in verifying the Riemann hypothesis. It was designed in
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.366 | 0.227 |
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".