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Record W4387604497 · doi:10.1515/9780887555503

Two Years Below the Horn

2017· book· en· W4387604497 on OpenAlexaboutno aff
W. Andrew Taylor

Bibliographic record

VenueUniversity of Manitoba Press eBooks · 2017
Typebook
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFrench hornPhysicsAcoustics

Abstract

fetched live from OpenAlex

In "Two Years Below the Horn," engineer Andrew Taylor vividly recounts his experiences and accomplishments during Operation Tabarin, a landmark British expedition to Antarctica to establish sovereignty and conduct science during the Second World War. When mental strain led the operation’s first commander to resign, Taylor—a military engineer with extensive prewar surveying experience—became the first and only Canadian to lead an Antarctic expedition. As commander of the operation, Taylor oversaw construction of the first permanent base on the Antarctic continent at Hope Bay. From there, he led four-man teams on two epic sledging journeys around James Ross Island,overcoming arduous conditions and correcting cartographic mistakes made by previous explorers. The editors’ detailed afterword draws on Taylor’s extensive personal papers to highlight Taylor’s achievements and document his significant contributions to polar science. This book will appeal to readers interested in the history of polar exploration, science, and sovereignty. It also sheds light on the little known contribution of a Canadian to a distant theatre of the Second World War. The wartime service of Major Taylor reveals important new details about a groundbreaking operation that laid the foundation for the British Antarctic Survey and marked a critical moment in the transition from the heroic to the modern scientific era in polar exploration.

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)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.982
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0060.006
Open science0.0010.003
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0500.022

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 head0.027
GPT teacher head0.216
Teacher spread0.189 · 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
Published2017
Admission routes1
Has abstractyes

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