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Record W7117116882 · doi:10.12692/ijb/15.2.1-10

Mitigation of adverse effects of heat stress in chillies by using glycine betaine

2019· article· en· W7117116882 on OpenAlexfundno aff
Tanveer Hussain, Choudhary Muhammad Ayyub, Ijaz Ahmad, Irfan Ali, Zaid Mustfa, Adeel Anwar, Aqeel Ahmad, Sohail Latif, Tanveer Iqbal

Bibliographic record

VenueInternational Journal of Biosciences (IJB) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Stress Responses and Tolerance
Canadian institutionsnot available
FundersUniversity of Agriculture, FaisalabadAlberta Agricultural Research Institute
KeywordsBetaineGlycineOsmoprotectantSeedlingHeat stressCrop

Abstract

fetched live from OpenAlex

Chilli (Capsicum spp.) is an important vegetable cum spice crop of the night shade family requiring 20-30 °C optimum temperatures for plant growth and development, usually growth starts retarding below 15°C or above 32°C temperature. Almost all growth stages of chilli plants are influenced by high temperatures ultimately leads to economic yield losses in final crop productivity. The experiment was carried out in growth chamber of mushroom lab, Institute of Horticultural Sciences, University of Agriculture, Faisalabad aiming at identifying the best glycine betaine treatment proved to be useful in coping with adversaries of high temperature stress in chillies. Chilli genotypes named as C-37, Uk-101, H-13 and jawala were grown and sprayed with different concentrations (0, 5, 10, 15 and 20 mM) of glycine betaine at the seedling stage under high temperature stress (40/32ºC day and night temperature) in growth chamber provided with controlled conditions. Various physiological attributes of chilli genotypes were recorded. Glycine betaine application @15mM was best for enhancing the heat tolerance potential of chilli genotypes under heat stress. Glycine betaine has also been proved effective in enhancing the heat tolerance potential under high temperature 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.122

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 head0.005
GPT teacher head0.222
Teacher spread0.217 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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