Past Forward: The First Land-Mine Detector that Actually Worked
Bibliographic record
Abstract
The invention of an effective land-mine detector came about after a tragedy on the beaches of Dundee, Scotland. In 1940, the British Army, fearing a German invasion, buried thousands of land mines along the coast. But it didn't notify its allies. Soldiers from the Polish 10th Armored Cavalry Brigade on a routine patrol of the beach were killed or injured when the land mines exploded. After the tragedy, a Polish electrical engineer named Józef Stanislaw Kosacki developed a portable mine detector that performed flawlessly in tests. It weighed less than 14 kilograms and operated much like the metal detectors used by beachcombers today. The devices quickly went into production and were shipped first to Egypt, where the Germans had created a vast minefield, called the Devil's Gardens, covering over 2,900 square kilometers. Kosacki's detector was able to clear mines twice as fast as previous methods. It's estimated to have saved thousands of lives by the end of the war. Canada, the United Kingdom, and the United States continued to use versions of it until 1991.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".