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Record W2833835809 · doi:10.5539/jas.v10n8p125

Management of Soil Mulch in Weed Suppression and Sugarcane Productivity

2018· article· en· W2833835809 on OpenAlexvenueno aff
Danilo César Oliveira De Cerqueira, Vilma Marques Ferreira, Renan Cantalice de Souza, João Correia de Araújo Neto, Vinícius Santos Gomes da Silva, Freds Fernando Alves de Almeida

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Management and Crop Yield
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsMulchAgronomyWeed controlProductivityWeedCrotalaria junceaCajanusSoil fertilityAgroforestryEnvironmental scienceBiologySoil waterGreen manureEcology

Abstract

fetched live from OpenAlex

The soil mulch is an agricultural practice that can benefit soil fertility and can be effective in suppressing weeds. The objective this research was to evaluate the mulching from legumes in weed control and sugarcane (first harvest/cut) productivity, comparing the results with the conventional application of herbicides. This research was carried out under field conditions. Five legumes were managed in two ways to form the soil cover: (1) mechanical topple, and (2) chemically desiccated. To compare the results, used treatments with herbicides applied in pre and pre + post emergence. The soil mulch from mechanical topple of Crotalaria spectabilis, C. juncea, C. ochroleuca, C. breviflora and Cajanus cajan presented lower efficiency in suppressing weeds than the treatment with herbicides applied in pre + post-emergence, however, were more efficient in controlling weeds in relation to the use of herbicides in pre-emergence, a fact observed at 60 days of sugarcane cultivation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.226
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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