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Record W6948212853 · doi:10.48579/pro/mlvpni

Occurrence of Cuban bee throughout more than a century (125 years) of data

2025· dataset· en· W6948212853 on OpenAlexaboutno aff

Bibliographic record

Venuedata.InDoRES · 2025
Typedataset
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsnot available
Fundersnot available
KeywordsNatural historyNational Museum of Natural HistoryNatural (archaeology)National museumDistribution (mathematics)George (robot)

Abstract

fetched live from OpenAlex

This database contains information on Cuban bees from historical collections of six natural history museums: Museo Nacional de Historia Natural de Cuba, Instituto de Ecología y Sistemática (Havana, Cuba), Muséum national d'Histoire naturelle, American Museum of Natural History, Smithsonian Institution, and the National Museum of Natural History, Washington, Kansas University, Natural History Museum, Kansas from three countries. In addition, it includes information from collections made by the first author between 2018-2024, from the bibliography and online databases, with information from other institutions: Ontario, Toronto, York University, Packer Collection, Florida, Gainesville, Division of Plant Industry, Florida State Collection of Arthropods, Los Angeles County Museum of Natural History, Cornell University Insect Collection, Illinois Natural History Survey, National Museum of Natural History and Museum of Comparative Zoology. The database includes 1322 records, of which 1067 are new records, of bees from various localities in the Cuban provinces of Havana, Mayabeque and Artemisa. Fiftytwo species distributed in 23 genera and 4 families were recorded. For these species, the database provides information on ecology, biogeographic distribution and the museum where the specimens are kept. Coordinates (GPS) from a single point are provided for the 117 localities where species were recorded.

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)0.003
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.011
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.012

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 head0.152
GPT teacher head0.438
Teacher spread0.286 · 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
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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