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Record W4413046293 · doi:10.7202/1118676ar

Wild and Radioactive Scenery: Nuclearized Landscapes of the Western United States

2025· article· en· W4413046293 on OpenAlexvenueno aff
Lucie Genay

Bibliographic record

VenueIntermédialités Histoire et théorie des arts des lettres et des techniques · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRadioactive contamination and transfer
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyArchaeologyEnvironmental planning

Abstract

fetched live from OpenAlex

The West of the United States is world-renowned for its breathtaking scenery and legendary wilderness immortalized through art and literature, conservationist efforts, and tourism. During World War II and the Cold War, the western deserts that had already captured the imagination of travelers, artists, and tourists alike also became major assets in building the US nuclear military industrial complex. A novel, anthropogenic form of landscape was then invented in the region, which acquired a new identity as the “atomic West” and, in turn, led to the emergence of new kinds of tourism and artistic representations of nuclear spaces. Based on a selection of sites, this article critically studies the crossroads between radioactivity and wilderness in the western United States. It asks how western landscapes and desertscapes were nuclearized and what were some of the cultural, environmental, political, and economic implications of this process.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.004
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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