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Record W3216842361 · doi:10.15377/2409-9813.2021.08.11

Biofumigation for the Control of Vegetables Soilborne Pathogens in Some non-Temperate Climate Countries

2021· article· en· W3216842361 on OpenAlexaboutno aff
M. Besri

Bibliographic record

VenueGlobal Journal of Agricultural Innovation Research & Development · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Disease Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTemperate climateEnvironmental scienceBiotechnologyEnvironmental protectionToxicologyBiologyEcology

Abstract

fetched live from OpenAlex

Soil-borne pathogens (SBPs) significantly reduce the yield and quality of crops worldwide. In the past, their control was principally accomplished by using soil fumigants such as methyl bromide (MB). However, this fumigant which is a powerful ozone-depleting substance, has completely been phased out under the Montreal Protocol (MP). New chemicals and non-chemical alternatives to MB, including biofumigation, have been actively researched, developed, and commercially adopted worldwide. This review seeks to provide the status of biofumigation for the control of SBPs in some non-temperate climate zones referred to in this paper as Article 5 countries or developing countries according to the Montreal Protocol (MP) classification. The review will first define “the non-temperate climate zone,” list the countries belonging to this zone, focus on the role and importance of the MP in phasing-out MB, and in searching and commercially adopting alternatives including biofumigation to this fumigant. It also describes the biofumigation techniques reported and used, reports its efficacy/inefficacy to manage SBPs in some non-climate temperate countries, insists on the place it must have in an IPM program to increase its efficacy, and finally, lists the collaboration and the research needed to further develop and commercially adopt this technology in non-temperate climate countries.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.034
GPT teacher head0.300
Teacher spread0.266 · 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 designObservational
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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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