MétaCan
Menu
Back to cohort
Record W3135282713 · doi:10.1126/sciadv.abe6636

Virtual pollination trade uncovers global dependence on biodiversity of developing countries

2021· article· en· W3135282713 on OpenAlexaff
Felipe Deodato da Silva e Silva, Luísa G. Carvalheiro, Jesús Aguirre‐Gutiérrez, Marc Lucotte, Karlo G. Guidoni‐Martins, Frédéric Mertens

Bibliographic record

VenueScience Advances · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité du Québec à Montréal
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekNatural Environment Research CouncilConselho Nacional de Desenvolvimento Científico e TecnológicoSight Research UK
KeywordsPollinationBiodiversityAstrobiologyBiologyBusinessEcologyPollen

Abstract

fetched live from OpenAlex

Nations' food consumption patterns are increasingly globalized and trade dependent. Natural resources used for agriculture (e.g., water, pollinators) are hence being virtually exchanged across countries. Inspired by the virtual water concept, we, herein, propose the concept of virtual biotic pollination flow as an indicator of countries' mutual dependence on biodiversity-based ecosystem services and provide an online tool to visualize trade flow. Using information on 55 pollinator-dependent crop markets (2001-2015), we show that countries with higher development level demand high levels of biodiversity-based services to sustain their consumption patterns. Such patterns are supported by importation of virtual biotic pollination (up to 40% of national imports of pollinator-dependent crops) from developing countries, stimulating cropland expansion. Quantifying virtual pollination flow can help develop new global socioeconomic policies to meet the interconnected challenges of biodiversity loss, ecosystem health, and social justice.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.241
Teacher spread0.211 · 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

Citations46
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueScience AdvancesSame topicPlant and animal studiesFrench-language works237,207