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Can Mushrooms and Their Derivatives Be Efficient Bioalternatives to Conventional Synthetic Insecticides? (Review)

2021· review· en· W4213289282 on OpenAlexaff
Aly El Sheikha

Bibliographic record

VenueInternational journal of medicinal mushrooms · 2021
Typereview
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsMcMaster UniversityUniversity of Ottawa
Fundersnot available
KeywordsMushroomInsectBiotechnologyEnvironmentally friendlyAgricultureBiologyRisk analysis (engineering)ToxicologyBusinessEcologyBotany

Abstract

fetched live from OpenAlex

Annual losses in both agricultural and economic sectors have increased dramatically because of insect pests; hence, the effects of these pests pose a major global threat. In addition, some control methods currently used to combat insect pests are no longer as effective due to the continuous evolution in resistance and to the serious damage of these control approaches on the ecological system. Therefore, there is an urgent need to find new avenues that are more effective and environmentally friendly. In the search for novel, effective, and natural insecticides, many mushroom species are substantial sources of biologically active compounds, including those that possess insecticidal activities. This review illustrates the potential role of mushrooms as promising and powerful bioinsecticidal agents.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.385
Teacher spread0.333 · 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.

Study designOther design
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

Citations19
Published2021
Admission routes1
Has abstractyes

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