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Record W4324045090 · doi:10.3897/bdj.11.e99650

Apoidea of the collections of Lyon, Aix-en-Provence, Marseille and Toulon Museums of Natural History (France)

2023· article· en· W4324045090 on OpenAlexaff
Jean-Yves Meunier, Benoît Geslin, Mehdi Issertes, Gilles Mahé, Frédéric Vyghen, Harold Labrique, Yves Dutour, Vincent Poncet, Jérémy Migliore, Gabriel Nève

Bibliographic record

VenueBiodiversity Data Journal · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsMusée de la Civilisation
Fundersnot available
KeywordsApoideaGeographyGenusNatural historyArchaeologyHymenopteraZoologyEcologyBiology

Abstract

fetched live from OpenAlex

Many insect species have shown dramatic declines over the last decades, as a result of man-related environmental changes. Many species which were formerly widespread are now rare. To document this trend with evidence, old records of collected specimens are vital. We provide here the data on 9752 bee (Hymenoptera: Apoidea) specimens hosted in several museums of south-east France: Musée des Confluences in Lyon, Muséum d’Histoire Naturelle de Marseille, Muséum d’Aix-en-Provence and the Muséum Départemental du Var in Toulon. Most of the specimens (9256) come from France and include data on 552 named species. For most of these specimens, the geographical location, including geographical coordinates, is based on the locality (town or village) where they were collected. The specimens were captured from the beginning of the nineteenth century to 2018. The identifications of 1377 specimens, mainly belonging to the genus Bombus , are considered reliable, as these were performed or been checked since 2009. All the other reported identifications are the original ones given by the original collectors.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0120.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0490.009

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 head0.060
GPT teacher head0.193
Teacher spread0.132 · 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 designObservational
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

Citations9
Published2023
Admission routes1
Has abstractyes

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