MétaCan
Menu
Back to cohort
Record W4307272918 · doi:10.3390/plants11212826

Effects of Graphene Oxide on Plant Growth: A Review

2022· review· en· W4307272918 on OpenAlex
Yan Yang, Runxuan Zhang, Xiao Zhang, Zezhong Chen, Haiyan Wang, Paul C. H. Li

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePlants · 2022
Typereview
Languageen
FieldEngineering
TopicGraphene and Nanomaterials Applications
Canadian institutionsSimon Fraser University
FundersShanxi Scholarship Council of China
KeywordsPlant growthAgricultureBiosafetyBiotechnologyNanotechnologyBiochemical engineeringBiologyEcologyEngineeringAgronomyMaterials science

Abstract

fetched live from OpenAlex

Several reports of graphene oxide (GO) promoting plant growth have sparked interest in its potential applications in agroforestry. However, there are still some toxicity studies that have raised concerns about the biosafety of GO. These reports show conflicting results from different perspectives, such as plant physiology, biochemistry, cytology, and molecular biology, regarding the beneficial and detrimental effects of GO on plant growth. Seemingly inconsistent studies make it difficult to effectively apply GO in agroforestry. Therefore, it is crucial to review and analyze the current literature on the impacts of GO on plant growth and its physiological parameters. Here, the biological effects of GO on plant growth are summarized. It is proposed that an appropriate concentration of GO may be conducive to its positive effects, and the particle size of GO should be considered when GO is applied in agricultural applications. This review provides a comprehensive understanding of the effects of GO on plant growth to facilitate its safe and effective use.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.025
GPT teacher head0.249
Teacher spread0.225 · 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