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Record W1987328595 · doi:10.4141/cjps06035

Acetyl salicylic acid alleviates chilling-induced damage in muskmelon seedlings

2007· article· en· W1987328595 on OpenAlexvenueno aff
Ahmet Korkmaz, Murat Uzunlu, Ali Rıza Demirkıran

Bibliographic record

VenueCanadian Journal of Plant Science · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Growth Enhancement Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCucumisSalicylic acidSeedlingShootHorticultureChemistryBiology

Abstract

fetched live from OpenAlex

Salicylic acid (SA) is a common plant-produced signal molecule that is responsible for inducing tolerance to a number of biotic and abiotic stresses. An experiment was, therefore, conducted to test whether acetyl salicylic acid (ASA) application at various concentrations through seed immersion or foliar spray would protect muskmelon [Cucumis melo L. (Reticulatus Group)] seedlings subjected to chilling stress. Twenty-one-day-old plants pre-treated with ASA (0, 0.1, 0.25, 0.50 or 1.0 mM) were subjected to chilling stress for 72 h at 3 ± 0.5°C. ASA, applied either through seed immersion or foliar spray, was effective within the range of 0.1 to 1 mM in inducing tolerance to chilling stress in muskmelon seedlings; however, there was no significant difference between application methods. ASA significantly and curvilinearly affected all seedling growth and stress indicator variables tested except shoot dry weight. The best protection was obtained from seedlings pre-treated with 0.5 mM ASA. The highest ASA concentration used was slightly less effective in providing chilling stress protection. Even though both methods provided similar means of protection, due to its simplicity and practicality, immersion of muskmelon seeds prior to sowing in 0.5 mM ASA would be a more desirable method to induce tolerance to chilling stress. Key words: Cucumis melo, aspirin, chilling stress tolerance, gas exchange, electrolyte leakage

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.224
Teacher spread0.204 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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