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Record W2767022314 · doi:10.3389/fpls.2017.01764

Interactions of Root-Feeding Insects with Fungal and Oomycete Plant Pathogens

2017· review· en· W2767022314 on OpenAlexafffund
Telsa Willsey, Syama Chatterton, Héctor A. Cárcamo

Bibliographic record

VenueFrontiers in Plant Science · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsUniversity of LethbridgeAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAlberta Pulse Growers CommissionAlberta Crop Industry Development Fund
KeywordsOomyceteBiologyPythiumContext (archaeology)Biological pest controlHerbivorePathosystemRhizospherePhyllosphereBiosecurityEcologyBotanyPathogenHost (biology)MicrobiologyBacteria

Abstract

fetched live from OpenAlex

Soilborne fungal and oomycete pathogens are the causal agents of several important plant diseases. Infection frequently co-occurs with herbivory by root-feeding insects, facilitating tripartite interactions that modify plant performance and mortality. In an agricultural context, interactions between pathogens, herbivores, and plants can have important consequences for yield protection. However, belowground interactions are inherently difficult to observe and are often overlooked. Here, we review the impact of direct and indirect interactions between root-associated insects, fungi, and oomycetes on the development of plant disease. We explore the relationship between insect feeding injury and pathogen infection, as well as the role of insects as vectors of fungal and oomycete pathogens. Synergistic interactions between insects and phytopathogens may be important in weed suppression, and we highlight several promising candidates for biocontrol. Bridging the gap between entomological and pathological research is a critical step in understanding how interactions between insects and microorganisms modify the community structure of the rhizosphere, and how this impacts plant functioning. Furthermore, the identification of belowground interactions is required to develop effective pest monitoring and management strategies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.065
GPT teacher head0.276
Teacher spread0.210 · 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 designNot applicable
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

Citations47
Published2017
Admission routes2
Has abstractyes

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