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The Nature of War

2017· book-chapter· en· W4234710812 on OpenAlexaboutno aff

Bibliographic record

VenueThe MIT Press eBooks · 2017
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicHistory and Developments in Astronomy
Canadian institutionsnot available
Fundersnot available
KeywordsIonosphereAdversaryNatural (archaeology)ShortwaveRelation (database)MeteorologyPolitical scienceHistoryGeographyComputer scienceGeophysicsGeologyPhysicsComputer security

Abstract

fetched live from OpenAlex

This chapter sets out the relationship between nature and machines that will run through the rest of the book. It explores how World War II researchers interpreted natural phenomena of all kinds through their effects on specific groups of machines. In the upper atmospheric research so crucial to wartime communications, radio scientists crafted a stripped-down “nature” that transformed ion distributions and atmospheric dynamics into weapons against the enemy. Those disruptions began as a generalized problem of northern regions, but they quickly turned into an issue of the anomalous and turbulent polar ionosphere and geophysics, and were finally transformed into a question of a uniquely Canadian natural order and its relation to radio failures. What emerged from WWII was a commitment to use ionospheric research as a way of articulating the precise connections between Canada’s northern nature and the shortwave disruptions that threatened the country as the Cold War took shape.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.953
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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