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Record W7134986242 · doi:10.5376/me.2024.15.0026

Insights into Mechanisms of Maize Resistance to Major Pests

2024· article· W7134986242 on OpenAlex
Jiamin Wang, Yunchao Huang

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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMolecular Entomology · 2024
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsResistance (ecology)Zea maysPlant disease resistanceInsecticide resistanceAdaptability

Abstract

fetched live from OpenAlex

Maize is a critical staple crop, providing food security and supporting economies worldwide. However, the crop faces persistent threats from various pests, leading to significant yield losses and environmental damage. This study explores the mechanisms of maize resistance to major pests, encompassing conventional breeding strategies, biochemical defenses, genetic and molecular tools, and anatomical traits. A case study on Bt maize highlights its role as a breakthrough in pest resistance, delving into its development, mechanisms of action, and socioeconomic impacts. Additionally, integrative approaches combining genetic, agronomic, and biological practices are discussed to enhance pest resistance. Challenges such as resistance evolution, regulatory hurdles, and the need for sustainable solutions are examined. The findings underscore the necessity of continuous innovation in breeding techniques and integrative pest management to ensure long-term maize productivity and sustainability.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.241
Teacher spread0.232 · 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