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Record W2943137051 · doi:10.5539/jas.v11n7p148

Weed Suppression by Cover Plants in the Amazonian

2019· article· en· W2943137051 on OpenAlexvenueno aff
Leandro Amorim Damasceno, J. E. B. de Carvalho, Francisco Alisson da Silva Xavier, Ansselmo Ferreira dos Santos, Gerlândio Suassuna Gonçalves, Alan Ferreira Leite de Lima, Wildson Benedito Mendes Brito, Cláudio Luiz Leone Azevedo, José Ferreira da Silva

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBrachiariaAgronomyCover cropWeedRandomized block designDry matterLeaf area indexWeed controlBiologyForageOrchardOrange (colour)Horticulture

Abstract

fetched live from OpenAlex

The productivity of citrus plants has not reached its maximum potential due to the action of several factors that directly affect agricultural profitability. Among these factors, weed interference has a great importance since it causes a reduction in crop productivity. The aim of this study was to assess the effect of different cover crops on suppression of weeds in an orange orchard. The research was conducted in a commercial orange production area located in Rio Preto da Eva, AM, Brazil. The experimental design was a randomized block design with six treatments and four replications. Treatments were control, millet, jack bean, forage turnip, brachiaria, millet + jack bean. The evaluated characteristics were weed density, soil cover percentage, total weed dry matter, total cover plant dry matter and growth indices (leaf area index, leaf area ratio, and specific leaf area). Cover plants provided a good dry matter production and had a suppressive effect on weed growth, except for forage turnip. Brachiaria and jack bean presented the highest values of leaf area index and leaf area ratio.

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 score0.985
Threshold uncertainty score0.208

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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

Citations23
Published2019
Admission routes1
Has abstractyes

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