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Record W2192098310 · doi:10.5539/jas.v8n1p188

Green Synthesis of Nano-Sized Sulfur and Its Effect on Plant Growth

2015· article· en· W2192098310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldMaterials Science
TopicStructural mechanics and materials
Canadian institutionsnot available
Fundersnot available
KeywordsSulfurMelia azedarachNanoparticleFourier transform infrared spectroscopyCucurbita pepoAqueous solutionNuclear chemistryScanning electron microscopeChemistryMaterials scienceNanotechnologyChemical engineeringBotanyOrganic chemistryBiology

Abstract

fetched live from OpenAlex

<p>The major objective of this research paper is to synthesis sulfur nanoparticles (SNPs) by green route, which can improve plant’s growth and reduce the environmental pathogens. Sulfur nanoparticles (SNPs) were synthesized by a simple green procedure using <em>Melia azedarach</em> leaves aqueous extract and citric acid. The leaves of <em>Melia azedarach</em> aqueous extract act as a capping and stabilizing agent in the formation of sulfur nanoparticles. The synthesized sulfur nanoparticles were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FT-IR). The average particles diameter size was found to be 20 ± 4 nm. Analysis showed that the nanoparticles are crystalline in nature, with spherical shape. A preliminary field study was conducted to evaluate growth and development of <em>Cucurbita pepo</em> in response to sulfur nanoparticles.</p>

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.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.016
GPT teacher head0.232
Teacher spread0.216 · 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