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Record W2794871773 · doi:10.3366/anh.2018.0484

Willughby's Buzzard: names and misnomers of the European Honey-buzzard (<i>Pernis apivorus</i>)

2018· article· en· W2794871773 on OpenAlexaff
T. R. Birkhead, Isabelle Charmantier, Peter Smith, Robert Montgomerie

Bibliographic record

VenueArchives of Natural History · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsBuzzardGeographyOrnithologyZoologyBiologyEcologySouthern Hemisphere

Abstract

fetched live from OpenAlex

The European Honey-buzzard (Pernis apivorus) was first accurately described and clearly distinguished from the Common Buzzard (Buteo buteo) by Francis Willughby and John Ray in their Ornithology, originally published in Latin in 1676. Alfred Newton's statement that Pierre Belon had described the species over a century earlier is not entirely correct, as Belon confused this honey-buzzard's features with those of the common buzzard and even appeared uncertain whether it was a separate species. One of Willughby's important contributions to ornithology was the identification and use of “characteristic marks” to distinguish and identify species, including those that distinguish the European Honey-buzzard from the Common Buzzard. Because Willughby provided the first accurate description of Pernis apivorus – and because his contribution to ornithology has never been formally recognized – we propose that the common name of the European Honey-buzzard be changed to Willughby's Buzzard.

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)0.002
metaresearch head score (Gemma)0.004
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: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0040.011
Scholarly communication0.0030.007
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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