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Record W4327814546 · doi:10.1002/ggn2.202200032

The Contribution of Digital Sequence Information to Conservation Biology: A Southern African Perspective

2023· article· en· W4327814546 on OpenAlexaboutno aff
Isa‐Rita M. Russo, Deon de Jager, Anna M. van Wyk, Arrie W. Klopper, Kenneth Uiseb, Coral Birss, Ian Rushworth, Paulette Bloomer

Bibliographic record

VenueAdvanced Genetics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsEuropean CommissionHigher Education Funding Council for WalesGlobal Challenges Research Fund
KeywordsPerspective (graphical)Sequence (biology)BiologyConservation biologyEvolutionary biologyComputational biologyGeographyZoologyComputer scienceGeneticsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Many recent contributions have made a compelling case that genetic diversity is not adequately reflected in international frameworks and policies, as well as in local governmental processes implementing such frameworks. Using digital sequence information (DSI) and other publicly available data is supported to assess genetic diversity, toward formulation of practical actions for long‐term conservation of biodiversity, with the particular goal of maintaining ecological and evolutionary processes. Given the inclusion of specific goals and targets regarding DSI in the latest draft of the Global Biodiversity Framework negotiated at the 15 th Conference of the Parties (COP15) in Montreal in December 2022 and the crucial decisions on access and benefit sharing to DSI that will be taken in the coming months and future COP meetings, a southern African perspective on how and why open access to DSI is essential for the conservation of intraspecific biodiversity (genetic diversity and structure) across country borders is provided.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.277

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.269
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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