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Record W3185419174 · doi:10.5539/jas.v13n8p91

Prevalence and Incidence of Cassava (Manihot esculenta) Brown Leaf Spot Disease Caused by Cercospora heningsii in Macuata Province, Vanua Levu, Fiji

2021· article· en· W3185419174 on OpenAlexvenueno aff
Rahul Ravneel Prasad, Mohseen Riaz Ud Dean, Bradley Alungo, Vinal Vishal Chand

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
Fundersnot available
KeywordsLeaf spotCercosporaManihot esculentaSubtropicsIncidence (geometry)BiologyCropTropicsOutbreakVeterinary medicineGeographyHorticultureAgronomyEcologyMedicine

Abstract

fetched live from OpenAlex

Cassava (Manihot esculenta Crantz) is a crop of many values in the tropical and subtropical regions of the globe. In Fiji, cassava is cultivated on vast acres of land but, the yield obtained is relatively lower because of many constraints, including the prevalence of diseases caused by the different pathogens. Among various pathogens responsible for a lower yield, the cassava brown leaf spot disease caused by Cercospora heningsii is responsible for causing enormous annual losses of cassava in tropical and subtropical regions. Because there is very little information regarding the association of the brown leaf spot disease and cassava in the country, the present study using survey as research instrument endeavors to determine the disease incidence and prevalence of brown leaf spot disease in the cassava fields of three villages (Mani Road, Boca, and Anuve) in the Bulileka area of the Macauta province in Vanua Levu, Fiji. The study found that brown leaf spot disease prevailed (100%) in all three villages. The percentage of disease incidence ranged from 36.4% to 42.9%. The maximum incidence (42.9%) of cassava brown leaf spot disease was found in Anuve village, followed by Mani Road village (38.2%), with the lowest disease incidence recorded for Boca village (36.4%).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.858
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.244
Teacher spread0.227 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

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