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Record W4386459836 · doi:10.1002/ppp3.10425

Small and in‐country herbaria are vital for accurate plant threat assessments: A case study from Peru

2023· article· en· W4386459836 on OpenAlexaff
Jay Delves, Joaquina Albán Castillo, Asunción Cano, Carmen Fernández Aviles, Edeline Gagnon, Paúl Gonzáles, Sandra Knapp, Blanca León, José Luís Marcelo Peña, Carlos Reynel, Rocío del Pilar Rojas Gonzáles, Eric F. Rodríguez Rodríguez, Tiina Särkinen, Rodolfo Vásquez, Peter W. Moonlight

Bibliographic record

VenuePlants People Planet · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant and Fungal Species Descriptions
Canadian institutionsUniversity of Guelph
FundersNational Geographic Society
KeywordsHerbariumIUCN Red ListGeographyConservation statusBiodiversityEndangered speciesCritically endangeredEcologyBiology

Abstract

fetched live from OpenAlex

Societal Impact Statement Herbaria can be considered plant libraries, each holding collections of dried specimens documenting plant diversity in space and time. For many plant species, these are our only evidence of their existence and the only means of assessing their conservation status. Specimens in all herbaria, especially those in small and often under‐resourced herbaria in megadiverse countries, are key to achieving accurate estimates of the conservation status of the world's plant species. They are also part of a country's shared heritage and critical contributions to knowledge of the world's diversity. Summary Internationally agreed targets to assess the conservation status of all plant species rely largely on digitised distribution data from specimens held in herbaria. Using taxonomically curated databases of herbarium specimen data for the mega‐diverse genera Begonia (Begoniaceae) and Solanum (Solanaceae) occurring in Peru, we test the value added from including data from local herbaria and herbaria of different sizes on estimations of threat status using International Union for Conservation of Nature (IUCN) Red List criteria. We find that the Global Biodiversity Information Facility (GBIF) has little data from Peruvian herbaria and adding these data influences the estimated threat status of these species, reducing the numbers of Critically Endangered and Vulnerable species in both genera. Similarly, adding data from small‐ and medium‐sized herbaria, whether in‐country or not, also improves the accuracy of threat assessments. [Correction added on 08 September 2023, after first online publication: In the preceding sentence, “litter” has been corrected to “little” in this version.] A renewed focus on resourcing and recognising the contribution of small and in‐country herbaria is required if we are to meet internationally agreed targets for plant conservation. We discuss our case study in the broader context of democratising and increasing participation in global botanical science.

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)0.010
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.042
GPT teacher head0.276
Teacher spread0.234 · 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

Citations32
Published2023
Admission routes1
Has abstractyes

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