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Record W1990761550 · doi:10.1556/aagr.60.2012.1.6

Cytokinin-cytokinin interaction ameliorates the callus induction and plant regeneration of tomato (Solanum lycopersicum Mill.)

2012· article· en· W1990761550 on OpenAlexfundno aff
Asep Indra Munawar Ali, T. Yossef, Antar El-Banna

Bibliographic record

VenueActa Agronomica Hungarica · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsHypocotylCallusCotyledonKinetinExplant cultureCytokininBiologyLycopersiconShootSolanumBotanyZeatinHorticultureAuxinIn vitro

Abstract

fetched live from OpenAlex

The present study was carried out for developing an efficient in vitro callus induction and plant regeneration system in four different tomato genotypes ( Solanum lycopersicum Mill., previous name: Lycopersicon esculentum ), Advantage II, Edkawy, Castle Rock and Super Strain B, using hypocotyl and cotyledon explants. The effects of two cytokinins, BA (benzyl adenine) and Kin (kinetin), on callus induction and plant regeneration frequency were investigated when added to MS medium in combination at varying concentrations. All concentrations of the two cytokinins were suitable for callus induction and plant regeneration. The frequency of callus induction and plant regeneration from both cotyledon and hypocotyl explants reached 100% for all tested genotypes. Cotyledons produced a higher average number of shoots per explants than hypocotyls for all the genotypes in the five concentrations of combined cytokinins. The average number of shoots per explant in Super Strain B was found to be the highest (42 and 60 for the hypocotyl and cotyledon explants, respectively). Supplementing MS medium with 1.0 mg L −1 kinetin and 1.0 mg L −1 benzyl adenine was found to be optimum for producing the highest number of shoots per explant from hypocotyls and cotyledons in the tomato genotypes investigated. The proposed medium showed a significant superiority over the reference media.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.408

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 head0.229
Teacher spread0.214 · 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

Citations14
Published2012
Admission routes1
Has abstractyes

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