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Record W2910195697 · doi:10.1126/science.aat6016

Agriculturally dominated landscapes reduce bee phylogenetic diversity and pollination services

2019· article· en· W2910195697 on OpenAlexaff
Heather Grab, Michael G. Branstetter, Nolan D. Amon, Katherine R. Urban‐Mead, Mia Park, Jason Gibbs, Eleanor J. Blitzer, Katja Poveda, Gregory M. Loeb, Bryan N. Danforth

Bibliographic record

VenueScience · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Manitoba
FundersDavid R. Atkinson Center for a Sustainable Future , Cornell UniversityAgricultural Research Service
KeywordsPhylogenetic diversityBiodiversityEcosystem servicesPollinationPollinatorPhylogenetic treeBiologyEcologyEcosystemAgroforestryPhylogeneticsDiversity (politics)GeographyPollen

Abstract

fetched live from OpenAlex

Land-use change threatens global biodiversity and may reshape the tree of life by favoring some lineages over others. Whether phylogenetic diversity loss compromises ecosystem service delivery remains unknown. We address this knowledge gap using extensive genomic, community, and crop datasets to examine relationships among land use, pollinator phylogenetic structure, and crop production. Pollinator communities in highly agricultural landscapes contain 230 million fewer years of evolutionary history; this loss was strongly associated with reduced crop yield and quality. Our study links landscape-mediated changes in the phylogenetic structure of natural communities to the disruption of ecosystem services. Measuring conservation success by species counts alone may fail to protect ecosystem functions and the full diversity of life from which they are derived.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.670

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 studies0.0010.000
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 head0.015
GPT teacher head0.190
Teacher spread0.174 · 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

Citations282
Published2019
Admission routes1
Has abstractyes

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