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Record W4311391024 · doi:10.3390/agronomy12123164

Potential of Omics to Control Diseases and Pests in the Coconut Tree

2022· article· en· W4311391024 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAgronomy · 2022
Typearticle
Languageen
FieldChemistry
TopicCoconut Research and Applications
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsWeevilBiologyCocos nuciferaRhynchophorusPalmCropCurculionidaeBiological pest controlBiopesticidePest controlBiotechnologyArecaceaePEST analysisBotanyAgronomyPesticideToxicology

Abstract

fetched live from OpenAlex

The coconut palm (Cocos nucifera L.) is a common crop in pantropical areas facing various challenges, one of them being the control of diseases and pests. Diseases such as bud rot caused by Phytophthora palmivora, lethal yellowing caused by phytoplasmas of the types 16SrIV-A, 16SrIV-D or 16SrIV-E, among others, and pests like the coconut palm weevil, Rhynchophorus vulneratus (Coleoptera: Curculionidae), and the horned beetle, Oryctes rhinocerus (Coleoptera: Scarabaeidae), are controlled by applying pesticides, pheromones and cultural control. These practices do not guarantee eradication since some causal agents have become resistant or are imbedded in infected tissues making them difficult to eradicate. This review condenses the current genomics, transcriptomics, proteomics and metabolomics studies which are being conducted with the aim of understanding the pathosystems associated with the coconut palm, highlighting the findings generated by omics studies that may become future targets for the control of diseases and pests in the coconut crop.

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.

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 score0.340
Threshold uncertainty score0.396

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 studies0.0000.000
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 head0.007
GPT teacher head0.240
Teacher spread0.234 · 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