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Record W3041621832 · doi:10.24908/iqurcp.14021

Blitzkrieg – An Army Fueled by Drugs

2020· article· en· W3041621832 on OpenAlexvenueno aff
Xavier Heckert

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2020
Typearticle
Languageen
FieldMedicine
TopicHistorical Medical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsBattleAdversaryPeacetimeLawPolitical scienceNazismPopulationEngineeringMedicineHistoryAncient historyComputer securityPoliticsComputer science

Abstract

fetched live from OpenAlex

Pervitin is a drug developed in Nazi Germany by the pharmaceutical company Temmler, before the start of World War II. Originally sold without prescription to the population, it was claimed to suppress fatigue, make one more alert, reduce hunger, and help fight depression. The main ingredient of this wonder drug was methamphetamine, the primary component of what we now call crystal meth. This miracle drug’s effectiveness against fatigue caught the attention of the director of the Research Institute of Defense Physiology of the German forces, Dr. Otto Ranke, who considered that fatigue was enemy number one of a soldier during battle. An order of thirty five million Pervitin tablets were purchased for the Wehrmacht’s invasion of France in May 1940 to increase effectivity of the campaign that relied especially on speed for success. History will claim that the use of mobile warfare over positional warfare with Germany’s motorized army, French high command mistakes, and an equipment disadvantage led to the ultimate defeat. My research aims to show that Pervitin was a crucial factor in the iron force of the blitzkrieg by the Wehrmacht and Luftwaffe, and that it did not as much come from its tactics, inferiority of the Allies, and employment of mobile warfare but from an army that was blitzed on Pervitin to turn it into a steamroller of a machine that could not be stopped, day or night.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.141
GPT teacher head0.408
Teacher spread0.268 · 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
GenreEmpirical

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
Published2020
Admission routes1
Has abstractyes

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