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Record W2946701023 · doi:10.1093/jxb/erz242

Current approaches to measure nitric oxide in plants

2019· review· en· W2946701023 on OpenAlexaff
Abhaypratap Vishwakarma, Aakanksha Wany, Sonika Pandey, Mallesham Bulle, Aprajita Kumari, Abir U. Igamberdiev, Luis A. J. Mur, Kapuganti Jagadis Gupta

Bibliographic record

VenueJournal of Experimental Botany · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsMemorial University of Newfoundland
FundersScience and Engineering Research BoardBiotechnology and Biological Sciences Research Council
KeywordsSignallingNitric oxideRadicalMeasure (data warehouse)Plant growthChemistryBiologyBiochemistryComputer scienceCell biologyBotanyData miningOrganic chemistry

Abstract

fetched live from OpenAlex

Nitric oxide (NO) is now established as an important signalling molecule in plants where it influences growth, development, and responses to stress. Despite extensive research, the most appropriate methods to measure and localize these signalling radicals are debated and still need investigation. Many confounding factors such as the presence of other reactive intermediates, scavenging enzymes, and compartmentation influence how accurately each can be measured. Further, these signalling radicals have short half-lives ranging from seconds to minutes based on the cellular redox condition. Hence, it is necessary to use sensitive and specific methods in order to understand the contribution of each signalling molecule to various biological processes. In this review, we summarize the current knowledge on NO measurement in plant samples, via various methods. We also discuss advantages, limitations, and wider applications of each method.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.436

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 science0.0010.000
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 head0.251
GPT teacher head0.323
Teacher spread0.072 · 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 designOther design
Domainnot available
GenreReview

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

Citations53
Published2019
Admission routes1
Has abstractyes

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