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Record W4386798128 · doi:10.21694/2379-2914.22002

A Path to Space: From Tsiolkovsky to Armstrong

2022· article· en· W4386798128 on OpenAlexaboutno aff

Bibliographic record

VenueAmerican Research Journal of History and Culture · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicHistory and Developments in Astronomy
Canadian institutionsnot available
FundersAustralian GovernmentNational Aeronautics and Space Administration
KeywordsPath (computing)Space (punctuation)Computer scienceComputer network

Abstract

fetched live from OpenAlex

The United Soviet Socialist Republic (USSR) and the United States (US) led the Space Race during the 20th century at the height of the Cold War.These adversaries raced to be the first to achieve spaceflight capabilities.The concept of space travel would provide an unprecedented experience and political might.Due to events in history at that time, military technologies aided advancements.The innovation resulted in creative ways to address fundamental questions, yet more importantly, would prove one nation dominate.Many extraordinary people pioneered this venture, including physicist and engineer Robert Goddard from the United States and Hermann Oberth, an Austro-Hungarian-born German physicist and engineer.In 1962, the United States achieved the first interplanetary flyby when Mariner 2 sped past Venus.Soon after, the Soviets sent the first woman into space, Valentina Tereshkova, in 1963.Additionally, other nations launched their rockets and satellites, including Canada in 1962, France in 1965, and Japan and China in 1970.The Russians led the race from Sputnik to the first Moon landing on July 16, 1969, when US astronauts Neil Armstrong and Erwin "Buzz" Aldrin touched down on the Moon's surface.Each nation made incredible advancements.Hard work and great focus brought the dreams of space travel to reality.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.010
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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
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
Published2022
Admission routes1
Has abstractyes

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