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Record W3023853601 · doi:10.1016/j.tree.2020.04.003

Disease-mediated ecosystem services: Pathogens, plants, and people

2020· article· en· W3023853601 on OpenAlexaff
Rachel E. Paseka, Lauren A. White, Dedmer B. Van de Waal, Alexander T. Strauss, Angélica L. González, Rebecca A. Everett, Angela Peace, Eric W. Seabloom, Thijs Frenken, Elizabeth T. Borer

Bibliographic record

VenueTrends in Ecology & Evolution · 2020
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversity of Windsor
FundersNational Socio-Environmental Synthesis CenterNational Science Foundation
KeywordsEcosystem servicesProvisioningEcosystemEnvironmental resource managementAutotrophEcosystem healthBusinessEcologyBiologyEnvironmental planningGeographyEnvironmental scienceComputer science

Abstract

fetched live from OpenAlex

Despite the ubiquity of pathogens in ecological systems, their roles in influencing ecosystem services are often overlooked. Pathogens that infect primary producers (i.e., plants, algae, cyanobacteria) can have particularly strong effects because autotrophs are responsible for a wide range of provisioning, regulating, and cultural services. We review the roles of pathogens in mediating ecosystem services provided by autotrophs and outline scenarios in which infection may lead to unexpected outcomes in response to global change. Our synthesis highlights a deficit of information on this topic, and we outline a vision for future research that includes integrative theory and cross-system empirical studies. Ultimately, knowledge about the mediating roles of pathogens on ecosystem services should inform environmental policy and practice.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations69
Published2020
Admission routes1
Has abstractyes

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