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Record W2574332954 · doi:10.5539/jps.v6n1p58

Interaction of Nitric Oxide with Phytohormones under Drought Stress

2017· article· en· W2574332954 on OpenAlexvenueno aff
Srivani S. Adimulam, Bhatnagar-Mathur Pooja, Parankusam Santisree

Bibliographic record

VenueJournal of Plant Studies · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsnot available
FundersConsortium of International Agricultural Research Centers
KeywordsDrought stressAbiotic stressNitric oxideAbiotic componentCoping (psychology)SalinityDrought resistanceStress (linguistics)Drought toleranceHeavy metalsBiologyEnvironmental scienceAgronomyChemistryEcologyEnvironmental chemistryPsychology

Abstract

fetched live from OpenAlex

Plants are often exposed to a plethora of stress conditions such as salinity, extreme temperatures, drought, and heavy metals that can greatly impact farmer’s income. Nitric oxide (NO) has been implicated in resistance to various plant stresses and hence gaining increasing attention from plant researchers. NO mediate various abiotic and biotic stresses in plants including drought stress. However, it is still unclear about the actual involvement of NO in drought stress responses at a whole plant level. Whether NO act alone or in coherence with other phytohormones and signaling molecules is an open question till now. Here we summarized the interaction of NO with the well-known phytohormones in coping with the drought stress.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.050
GPT teacher head0.281
Teacher spread0.231 · 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 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

Citations5
Published2017
Admission routes1
Has abstractyes

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