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Record W4283328634 · doi:10.32370/ia_2022_06_13

History of England in the Rose "Anna Boleyn"

2022· article· en· W4283328634 on OpenAlexvenueno aff
Lyudmila Pet’ko, A. I. Maksymenko

Bibliographic record

VenueIntellectual Archive · 2022
Typearticle
Languageen
FieldNeuroscience
TopicUndergraduate Neuroscience Education and Research
Canadian institutionsnot available
Fundersnot available
KeywordsQueen (butterfly)WifeHistoryPoliticsArtHenry IV, Holy Roman EmperorArt historyPerformance artGenealogyLiteratureTheologyPhilosophyLaw

Abstract

fetched live from OpenAlex

Many English roses have names related to English people, places, even literary characters. The article devoted to the rose "Anne Boleyn" introduced by English hybridizer David Austin for the UK in 1999. He named this rose after Anne Boleyn, the Queen of England from 1533 to 1536 as the second wife of King Henry VIII. On 7 September 1533, she gave birth to the future Queen Elizabeth I. Given biological characteristic of English Rose Anne Boleyn. The authors describe what happened with Henry VIII, King of England, charming, attractive and even kind, for a member of the Royal family. Yet he is most remembered for being gluttonous, impaired and executing wives (Henry VIII and miscarriages; was it the Kell antigen?). Anne Boleyn is a mysterious figure in history. Queen Anne Boleyn is well-known for playing a key figure in the political and religious transformation in England about 500 years ago. Since her death in 1536, Anne Boleyn has appeared in various artistic mediums, with each decade contributing yet another incarnation of the late queen. Other queens of the world have seen their moments in the spotlight come and go sporadically, but Anne Boleyn remains a constant. The authors write that Anne Boleyn is an intriguing historical figure who has been the subject of many biographies, novels, paintings, films, and television series, a tragic opera "Anna Bolena" (1830) composed by Gaetano Donizetti.

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)0.003
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.297
Teacher spread0.207 · 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
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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