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Record W4398786934 · doi:10.5376/me.2024.15.0004

Advances in Biological Control Methods for Managing Sugarcane Insects

2024· article· en· W4398786934 on OpenAlexvenueno aff
Yulin Zhou

Bibliographic record

VenueMolecular Entomology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
Fundersnot available
KeywordsBiological pest controlBiologyBiotechnologyEcology

Abstract

fetched live from OpenAlex

The objective of this systematic review is to explore recent advances in biological control methods for managing sugarcane insect pests and to evaluate their role in integrated pest management (IPM). By synthesizing current research, this review highlights key biological control agents and their efficacy against major sugarcane insect pests, emphasizing classical, augmentative, and conservation strategies. Classical biological control approaches focus on the introduction of exotic natural enemies, such as parasitoids and predators, which have shown significant success in managing pests like the sugarcane borer ( Diatraea saccharalis ) and root borer ( Diaprepes abbreviatus ). Augmentative strategies involve mass rearing and periodic release of natural enemies like Trichogramma  spp. and Cotesia flavipes , which have proven effective in reducing pest populations. Conservation biological control emphasizes habitat management practices that enhance the survival and efficacy of native and introduced natural enemies. Furthermore, microbial control agents such as entomopathogenic fungi ( Beauveria bassiana ), bacteria ( Bacillus thuringiensis ), and viruses are gaining prominence in sugarcane pest management due to their specificity and environmental safety. This review provides insights into the potential of these biological control methods in sustainable sugarcane pest management and underscores the importance of integrating them into broader IPM frameworks.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.235

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 head0.014
GPT teacher head0.311
Teacher spread0.296 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

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