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Record W2937736705 · doi:10.5539/jas.v11n5p314

Allelopathic Effect of Crambe (Crambe abyssinica) Extract on Corn Seedlings

2019· article· en· W2937736705 on OpenAlexvenueno aff
Bruna de Villa, Reginaldo Ferreira Santos, Deonir Secco, Jair Antônio Cruz Siqueira, Luciene Kazue Tokura, Maritane Prior, Alessandra Mayumi Tokura Alovisi, Laura Luana Foltz, Chaiane Bassegio, Willian Silva Melo, Mauricio Antonio Pauly, Leonardo da Silva Reis, Matheus Rodrigues Savioli, Laís Fernanda Juchem do Nascimento, Soni Willian Haupenthal

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsnot available
Fundersnot available
KeywordsCrambeShootGerminationAllelopathyBiologyCropAgronomyDry weightHorticultureStem-and-leaf display

Abstract

fetched live from OpenAlex

The crambe crop is an interesting option for the production of biodiesel, mainly due to the high oil content (35%) and the corn one of the most implanted crops in Brazil. Thus, the objective of this work was to evaluate the allelopathic effect of crambe in the initial development of maize. The experiment was carried out in the laboratory of seed and plant evaluation (LASP) and laboratory of soil physics (LAFIS) of UNIOESTE-Cascavel. The seeds of corn with germination of 93.5% were submitted to four concentrations plus the control in the different parts of the crambe plant, where the growth and mass of corn were valued. The experimental design was completely randomized with 4 replicates per treatment. The statistical analysis consisted of analysis of variance and the means of the treatments were compared by the Tukey test at 5% of significance. The extract of the whole crambe plant stimulated aerial length, root length, fresh and dry mass of corn shoot. Root, stem and leaf extracts inhibited the initial development of maize in relation to growth and fresh and dry shoot mass.

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 score0.898
Threshold uncertainty score0.423

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 communication0.0000.001
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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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