Cracking the Nazi code the untold story of Agent A12 and the solving of the Holocaust code
Bibliographic record
Abstract
"The thrilling true story of Canada's greatest spy, Agent A12. In public life, Nova Scotian Dr. Winthrop Bell was a wealthy businessman and Harvard philosophy professor. As MI6 Secret Agent A12, he dodged gunfire and shook pursuers to break open the emerging Nazi conspiracy in electrifying 1919 Berlin. Under cover as a Reuters reporter, he interviewed royalty, military informants, and intellectuals like Albert Einstein and Edith Stein. He followed clues to crack a deadly mystery and sounded the earliest warning of the Nazi plot for WWII. His reports went directly to the man known as C, the legendary founder of MI6, as well as to the prime ministers of Britain and Canada. But a powerful fascist politician quietly suppressed his alerts. Bell became a spy once again in the face of WWII. In 1939, he was the first to crack Hitler's deadliest secret code: the Holocaust. At that time the Führer was a popular politician who said he wanted peace. Could anyone believe Bell's shocking warning? Fighting an epic intelligence war from Ukraine, Russia, Poland and the Baltic to France, Germany, Canada and Washington, D.C., A12 was the real-life 007, waging a single-handed fight against madmen bent on destroying the world. Without Bell's astounding courage, the Nazis could have won the war. Cracking the Nazi Code is the first book to illuminate the exploits of Winthrop Bell, Agent A12"--
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".