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Record W2740539023 · doi:10.5206/wurjhns.2017-18.1

Plant Warfare: Allelopathic Effects of Nicotiana tabacum on the Germination of Vigna radiata and Triticum aestivum

2017· article· en· W2740539023 on OpenAlexaffvenue
Jason M Baek, Olivia J Kawecki, Krishawn D Lubin, Jialin Song, Olivia A Wiens, Fiona Wu

Bibliographic record

VenueWestern Undergraduate Research Journal Health and Natural Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsWestern University
Fundersnot available
KeywordsAllelopathyGerminationVignaRadiataBiologyNicotiana tabacumCropAgronomyBioassayHorticultureBotany

Abstract

fetched live from OpenAlex

Allelopathic chemicals in many plants can be released into the surrounding soil and may affect the development of nearby crops. Understanding allelopathic effects can be extremely beneficial for both economic and environmental reasons. The information gained from understanding allelopathy may be used in the optimization of crop rotations. Previous studies have investigated the negative allelopathic effects of Nicotiana tabacum — tobacco, on corn and other crops. Our study investigated the allelopathic impact of tobacco on seed germination of mung bean, Vigna radiata, and organic red fife wheat, Triticum aestivum. Seeds were treated with various concentrations of tobacco leaf solution to study the effects of tobacco on germination of the seeds. The effects of tobacco on the germination rate and percentage of germination were analyzed using one-way ANOVA with Tukey HSD tests. Results showed that there was a significant decrease in germination rate at high concentrations of tobacco. Our findings suggest that allelopathic chemicals released by tobacco have detrimental effects on the germination of mung bean and red fife wheat. Therefore, when forming crop rotations, it is important to take into account and understand the allelopathic effects and interactions between different species planted in the same region.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.364
Teacher spread0.298 · 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.

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

Citations8
Published2017
Admission routes2
Has abstractyes

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Same venueWestern Undergraduate Research Journal Health and Natural SciencesSame topicAllelopathy and phytotoxic interactionsFrench-language works237,207