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Record W2754629731 · doi:10.6000/1927-5129.2017.13.81

Effect of Centaurea pullata Methanolic Extract on the Growth of Portulaca oleracea

2017· article· en· W2754629731 on OpenAlexvenueno aff
Wasi Ullah Khan, Rahmat Ali Khan, Safir Ullah Khan

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAllelopathy and phytotoxic interactions
Canadian institutionsnot available
Fundersnot available
KeywordsPortulacaPhytotoxicityCentaureaAllelopathyChemistryShootAsteraceaeBotanyBioassayGerminationHorticultureBiology

Abstract

fetched live from OpenAlex

Phytotoxicity or allelopathy means poisonous results by a composite onplantgrowth, composites may be trace metals, pesticides salinity or phytotoxins. Some of the medicinal plants have phytotoxic activities which inhibit the growth of weeds and unwanted plants which are not of our desire. The present study is aimed to investigate the phytotoxic assessment of Centaurea pullata methanolic extract (CPME) roots. Dried plant were ground and extracted with methanol to prepare methanol crude extract. In-vitro phytotoxicity activity was conducted using these methanolic extracts as per standard procedures. The inhibitory effect of Centaurea pullata extract is tested on stalk and root of Portulaca oleracea and using four concentrations (3, 1.5, 0.75 and 0.37mg/ml) of plant extract and distal water in control. The result is noted on 5th and 10th days. The results obtained from these experiments showed that the crude methanolic extract of Centaurea pullata slightly inhibits the roots and shoots of Portulaca oleracea seeds as compared to the control plate which was not treated by the above mentioned sample extracts shown in Figures as. From the results obtained that, Phytotoxicity activity of Centaurea pullata methanolic extract showed non-significant results. Purification and in vivo studies of these plant are required for further verification.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.030
GPT teacher head0.277
Teacher spread0.247 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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