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Record W2994942004 · doi:10.1139/cjps-2019-0084

Effect of calcium and boron on growth and development of callus and shoot regeneration of date palm ‘Barhee’

2019· article· en· W2994942004 on OpenAlexvenueno aff
Ahmed Madi Waheed Al–Mayahi

Bibliographic record

VenueCanadian Journal of Plant Science · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSeed Germination and Physiology
Canadian institutionsnot available
Fundersnot available
KeywordsShootCalciumCallusBoric acidBrowningHorticultureOrganogenesisBotanyDry weightChemistryMicropropagationTissue cultureBiologyAnimal scienceFood scienceIn vitroBiochemistry

Abstract

fetched live from OpenAlex

There are some major obstacles in practical applications of date palm tissue culture in the laboratory such as reduced number of shoots, vitrification of tissues, and browning of cultured tissues. The objective of the present study is to determine the effect of boron (B) and calcium (Ca) on enhancing organogenesis of buds from callus, shoot multiplication, and phytochemicals of in vitro cultures of date palm ‘Barhee’. Addition of calcium chloride and boric acid to the medium was most effective on callus for shoot regeneration and number of shoots per jar, where the best result (88.89%, 8.2 shoots per jar, respectively) was obtained by using 660 mg L −1 Ca + 9.3 mg L −1 B, compared with other treatments. It was observed that the interaction between 660 mg L −1 Ca + 9.3 mg L −1 B resulted in the highest tissue content of B [(4.75 mg L −1 dry weight (DW)]. The highest medium calcium chloride level (880 mg L −1 ) increased Ca content in the shoots. The combined application had the effect in reducing the vitrification. Application of Ca and B induced synthesis of proteins bands with molecular weights of 88.0, 55.6, 52.8, 43.0, 41.1, 38.3, 28.3, and 16.6 KDa in a medium supplemented with 660 mg L −1 Ca + 9.3 mg L −1 B.

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 score0.656
Threshold uncertainty score0.105

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 head0.015
GPT teacher head0.217
Teacher spread0.203 · 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

Citations25
Published2019
Admission routes1
Has abstractyes

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