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Record W4311861719 · doi:10.18488/ijsar.v9i4.3220

Epidemiological Assessment of Cassava Mosaic Disease in a Savanna Region of the Democratic Republic of Congo

2022· article· en· W4311861719 on OpenAlexaff
Clara Funny Biola, Remy Tshibingu Mukendi, A. Kalonji-Mbuyi, K. K. Nkongolo

Bibliographic record

VenueInternational Journal of Sustainable Agricultural Research · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsLaurentian University
Fundersnot available
KeywordsWhiteflyGeographyIncidence (geometry)Manihot esculentaEpidemiologyVeterinary medicineOutbreakMosaicBiologyMedicineAgronomyHorticultureVirologyMathematicsPathology

Abstract

fetched live from OpenAlex

An epidemiological survey was conducted, from September 2009 to January 2010 in 206 famers’ fields located across 21 cassava-growing localities of Ngandajika territory in Lomami province (central part of Democratic Republic of Congo (DRC), to determine distribution and status of Cassava Mosaic Disease (CMD). Parameters related to the identification and the evaluation of CMD (incidence, severity and gravity) and number of adult whitefly vector were assessed. CMD was present in all localities surveyed and varied according to the localities and varieties. CMD incidence was low at the INERA station (4.33%) but high in Kafumbu (74.55%). Disease severity was low at the INERA station (1.09) but high in Kafumbu (2.67). Gravity was low at the INERA station (2.99%) and elevated in Quartier Congo (67.82%). The mean of adult whitefly populations varied with sites. However, the whiteflies were more abundant in Mpunga (3.65) compared to the INERA station (1.09). Overall, 71 % of varieties showed varying degrees of sensitivity to CMD. The results of this study revealed that the health status of cassava in Ngandajika is alarming and deserves sustained intervention. Adequate and effective control methods must to be in place to reduce the inoculum levels and the speed of CMD propagation and to limit yield losses caused by this disease.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.069
GPT teacher head0.365
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 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
Published2022
Admission routes1
Has abstractyes

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