Lightning down a World War II story of survival
Bibliographic record
Abstract
"The incredible true story of fighter pilot Joe Moser's war in the sky and secret survival at Buchenwald during World War II. On August 13, 1944, Joe Moser set off on his 44th combat mission over occupied France. Soon, he would join almost 170 other Allied airmen as prisoners in Buchenwald, one of the most notorious and deadly of Nazi concentration camps. Tom Clavin's Lightning Down tells this largely untold and riveting true story. Moser was just 22 years old, a farmboy from Washington State who fell in love with flying. During the war he realized his dream of piloting a P-38 Lightning, one of the most effective weapons the Army Air Corps had against the powerful German Luftwaffe. But on that hot August morning he had to bail out of his damaged, burning plane. Captured immediately, Moser's journey into hell began. Joe Moser and his courageous comrades from England, Canada, New Zealand, and elsewhere endured against impossible odds in the most horrific surroundings... until the day the orders are issued by Hitler himself to execute them. Only a most desperate plan might save them. The page-turning momentum of Lightning Down is like that of a thriller, but the stories of imprisoned and brutalized airmen are true and told in unforgettable detail, led by the distinctly American voice of Joe Moser, who prays every day to be reunited with his family. Lightning Down is a can't-put-down inspiring saga of brave men confronting great evil and great odds against survival"--
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".