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Record W2920859933 · doi:10.5539/jas.v11n4p485

Host Suitability of Weeds to the Root Lesions Nematoid in Soybean Areas in the North of Goias, Brazil

2019· article· en· W2920859933 on OpenAlexvenueno aff
Rafael Matias da Silva, Anderli Divina Ferreira Rios, Wilian Henrique Diniz Buso, Alan Soares Machado, Grasiele T. da Silva, Matheus Vinícius Abadia Ventura, Emizael Menezes de Almeida, Kênia Lorrany Trindade, Estevam Matheus Costa

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicNematode management and characterization studies
Canadian institutionsnot available
Fundersnot available
KeywordsWeedBiologyInfestationNematodePopulationAgronomyHost (biology)CropWeed controlBotanyEcology

Abstract

fetched live from OpenAlex

Several studies have done more on weed and nematode hosts. It is important to know a host of weeds and P. brachyurus in areas cultivated with soy. This nematode can stay in weed roots even in the absence of plants grown in the off-season, making it difficult to control them. The objective was to evaluate the host suitability of emerged weed species in cultivated areas with a soybean crop to the P. brachyurus nematode under natural infestation conditions. The surveys were conducted in commercial properties located in the municipalities of Campinorte and Rialma, both in the northern region of the state of Goias, Brazil under the no-tillage and conventional system, respectively, with a history of high nematode population densities. We evaluated 19 weed species with the highest expressivity in the properties. The population densities of the nematode ranged RFom 23 to 17,113 and 55 to 4,221 specimens per 10 grams of roots respectively. All as weed species evaluated as hosts of P. brachyurus. As species, Hyptis suaveolens, Sida cardifolia, Senna occidentalis, Coneyza canadensis and Commelina benghalensis had low population densities.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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