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Record W2944225073 · doi:10.5539/jas.v11n7p281

Study of Plants With Allelopathic Potential in the Initial Development of Lettuce

2019· article· en· W2944225073 on OpenAlexvenueno aff
Bruna de Villa, Deonir Secco, Luciene Kazue Tokura, Alessandra Mayumi Tokura Alovisi, Maritane Prior, Maurício Antônio Pilatti, Carlos Henrique de Oliveira Paz, Eduardo Lange Sutil, Diandra Ganascini, Everton Ortiz Rocha, Laíza Cavalcante de Albuquerque Silva, Luana Cristina Calliari Leite, Iván Werncke, Laís Fernanda Juchem do Nascimento, Mauricio Antonio Pauly, Tatiane Pauly, Maurício Ivan Cruz

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAllelopathyResistance (ecology)BiologyBotanyAgronomyGermination

Abstract

fetched live from OpenAlex

The inappropriate use of herbicides has increased the resistance of weeds; thus, the study of allelopathy becomes of paramount importance. The ability of certain plants to interfere with the metabolism of others by means of substances released into the environment, either by their aerial or roots system, becomes an alternative to combat invasive plants, dispensing with or reducing the use of herbicides. The objective of this work was to evaluate the plant species most sensitive to the allelopathic potential of aqueous extracts. One of the plant species studied was lettuce because it had a rapid response potential, thus showing the benefits obtained through allelopathy.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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 integrity0.0000.001
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 head0.024
GPT teacher head0.251
Teacher spread0.227 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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