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Record W2159172120 · doi:10.1073/pnas.0507165103

Pollination decays in biodiversity hotspots

2006· article· en· W2159172120 on OpenAlexaff
Jana C. Vamosi, Tiffany M. Knight, Janette A. Steets, Susan J. Mazer, Martin Burd, Tia‐Lynn Ashman

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of Calgary
FundersNational Science Foundation
KeywordsPollenBiologyPollinatorPollinationInterspecific competitionBiodiversityEcologyCompetition (biology)Ecological nicheNiche differentiationSpecies richnessPollen sourceNicheHabitat

Abstract

fetched live from OpenAlex

As pollinators decline globally, competition for their services is expected to intensify, and this antagonism may be most severe where the number of plant species is the greatest. Using meta-analysis and comparative phylogenetic analysis, we provide a global-scale test of whether reproduction becomes more limited by pollen receipt (pollen limitation) as the number of coexisting plant species increases. As predicted, we find a significant positive relationship between pollen limitation and species richness. In addition, this pattern is particularly strong for species that are obligately outcrossing and for trees relative to herbs or shrubs. We suggest that plants occurring in species-rich communities may be more prone to pollen limitation because of interspecific competition for pollinators. As a consequence, plants in biodiversity hotspots may have a higher risk of extinction and/or experience increased selection pressure to specialize on certain pollinators or diversify into different phenological niches. The combination of higher pollen limitation and habitat destruction represents a dual risk to tropical plant species that has not been previously identified.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.064
GPT teacher head0.243
Teacher spread0.179 · 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

Citations323
Published2006
Admission routes1
Has abstractyes

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