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Record W4386882996 · doi:10.1126/sciadv.adh4686

Conserving avian evolutionary history can effectively safeguard future benefits for people

2023· article· en· W4386882996 on OpenAlexaff
Rikki Gumbs, Claudia L. Gray, Michael Hoffmann, Rafael Molina‐Venegas, Nisha Owen, Laura J. Pollock

Bibliographic record

VenueScience Advances · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsMcGill University
FundersNatural Environment Research Council
KeywordsSafeguardBusinessComputer scienceRisk analysis (engineering)Environmental planningComputer securityEnvironmental scienceInternational trade

Abstract

fetched live from OpenAlex

Phylogenetic diversity (PD)-the evolutionary history of a set of species-is conceptually linked to the maintenance of yet-to-be-discovered benefits from biodiversity or "option value." We used global phylogenetic and utilization data for birds to test the PD option value link, under the assumption that the performance of sets of PD-maximizing species at capturing known benefits is analogous to selecting the same species at a point in human history before these benefits were realized. PD performed better than random at capturing utilized bird species across 60% of tests, with performance linked to the phylogenetic dispersion and prevalence of each utilization category. Prioritizing threatened species for conservation by the PD they encapsulate performs comparably to prioritizing by their functional distinctiveness. However, species selected by each metric show low overlap, indicating that we should conserve both components of biodiversity to effectively conserve a variety of uses. Our findings provide empirical support for the link between evolutionary history and benefits for future generations.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.010
GPT teacher head0.228
Teacher spread0.219 · 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 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

Citations18
Published2023
Admission routes1
Has abstractyes

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