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Record W3019254589 · doi:10.1080/21533369.2020.1746090

The young Joseph Banks: naturalist explorer and scientist, 1766–1772

2019· article· en· W3019254589 on OpenAlexaboutno aff
Anna Agnarsdóttir

Bibliographic record

VenueJournal for Maritime Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBotany, Ecology, and Taxonomy Studies
Canadian institutionsnot available
FundersArts and Humanities Research Council
KeywordsNaturalismGeorge (robot)HistoryClassicsArt historyPhilosophy

Abstract

fetched live from OpenAlex

As a young man Joseph Banks (1743–1820), a wealthy landowner, decided to devote his life to natural history. He became one of the best-known naturalist explorers of the eighteenth century. During his twenties, from 1766 to 1772, he went on three voyages of exploration: to Labrador and Newfoundland in 1766, on the Endeavour circumnavigation with James Cook in 1768–71 and, finally, in 1772, he led the first British scientific expedition to Iceland. It was these expeditions, undertaken as a young man, which shaped his life. From them he gained fame, becoming one of the country’s most powerful and leading naturalists. This article also discusses the question of whether he was a scientist? He certainly believed himself to be one and according to the standards of the eighteenth century he was one, and people must be judged by the standard of their age. In the modern sense of the word, without a university degree and with negligible publications in his lifetime, he was not one. His eventual strength lay in his influential position as President of the Royal Society, a friend of George III and a privy councillor, allowing him to organise new voyages of discovery and send botanists on missions to collect the world’s flora.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.002
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.004

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 head0.076
GPT teacher head0.327
Teacher spread0.250 · 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.

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

Citations1
Published2019
Admission routes1
Has abstractyes

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