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Record W4401152488 · doi:10.17161/bi.v18i.22332

Guideline Materials and Documentation for the Genetic Diversity Indicators of the Monitoring Framework for the Kunming-Montreal Global Biodiversity Framework

2024· article· en· W4401152488 on OpenAlexaboutno aff
Alicia Mastretta‐Yanes, Sofía Suárez, Rebecca Jordan, Sean Hoban, Jessica M. da Silva, Luis Castillo‐Reina, Myriam Heuertz, Fumiko Ishihama, Viktoria Köppä, Linda Laikre, Anna J. MacDonald, Joachim Mergeay, Ivan Paz‐Vinas, Gernot Segelbacher, Alicia Knapps, Henry Rakoczy, Amelie Weiler, Angelica Atsaves, Kira Cullmann, S Bagnato, Brenna R. Forester

Bibliographic record

VenueBiodiversity Informatics · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsnot available
FundersNaturvårdsverket
KeywordsGenetic diversityBiodiversityDocumentationDiversity (politics)Conservation geneticsEnvironmental resource managementPopulationGuidelineGeographyEnvironmental planningEcologyComputer scienceBiologyPolitical scienceMedicineEnvironmental scienceEnvironmental healthGenetics

Abstract

fetched live from OpenAlex

Genetic diversity is fundamental to biological diversity, vital for species’ health and adaptation to environmental change. Under the recently adopted Kunming-Montreal Global Biodiversity Framework (GBF), 196 Parties committed to report the status of genetic diversity for both wild and domesticated species. For this, three genetic diversity indicators were developed, two of which focus on processes contributing to genetic diversity conservation: ensuring that populations are large enough to maintain genetic diversity (effective population size Ne 500 indicator) and maintaining genetically distinct populations (populations maintained, PM indicator). A third indicator focuses on the number of species being monitored using DNA-based methods. Adopted by 196 CBD Parties in December 2022, GBF integrated Ne 500 and PM as headline and complementary indicators, respectively. To aid nations in quantifying these indicators, a detailed set of guideline materials was developed, encompassing species selection, data compilation, and indicator computation. These guidelines draw from the collaborative efforts of the first multinational assessment of genetic diversity indicators that was recently completed and that will be refined continually through a versioning system, as more experience is gained and shared. The materials aim to support the global monitoring framework established by the CBD and are accessible online for utilization and updates. The guidelines are available at this link.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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.

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

Citations13
Published2024
Admission routes1
Has abstractyes

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