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Record W2806681453 · doi:10.5539/jas.v10n7p258

Management of Meloidogyne javanica in Okra Using Compost of Pequi Fruit Waste

2018· article· en· W2806681453 on OpenAlexvenueno aff
Fabíola de Jesus Silva, Regina Cássia Ferreira Ribeiro, Adelica Aparecida Xavier, José Augusto Santos Neto, Claudia Maria da Silva, Édson Hiydu Mizobutsi

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas Gerais
KeywordsCompostMeloidogyne javanicaTransplantingRandomized block designChicken manureManureAgronomyDry weightOrganic fertilizerShootCow dungFertilizerHorticultureBiologyChemistrySowing

Abstract

fetched live from OpenAlex

Pequi waste added to soil can lead to promising results in the management of plant nematodes. This study evaluated the effect of organic compost of pequi fruit waste in the control of Meloidogyne javanica in okra plants. The compost was comprised of cattle manure, sugarcane straw, and pequi rind waste in the ratio 1:1:1. Treatments were five doses of organic compost (0, 5, 10, 20, and 30 g dm-3) and two additional controls: manure (20 g dm-3) and mineral fertilizer (100 mg dm-3 of N), arranged in randomized block design with 10 repetitions. Different treatments were incorporated into pots containing 3 dm-3 of sandy soil infested with 5,000 eggs of M. javanica. Seedlings were transplanted five days later, and evaluated after 60 days of transplanting. Organic compost with pequi waste incorporated to soil increased shoot dry weight and root weight, and reduced the number of egg masses, galls and eggs of M. javanica per gram of root, and reproduction factor. Doses of 20 and 30 g dm-3 increased plant development and reduced the reproductivity of M. javanica compared to mineral fertilizer.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.030
GPT teacher head0.256
Teacher spread0.226 · 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 designBench or experimental
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
Published2018
Admission routes1
Has abstractyes

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