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In Vitro Micropropagation of Jatropha curcas L. from Bud Aggregates

2013· article· en· W2147677053 on OpenAlexvenueno aff
Samson Daudet Medza Mve, Guy Mergeai, Philippe Druart, Jean‐Pierre Baudoin, André Toussaint

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsnot available
FundersUniversité de Liège
KeywordsJatropha curcasMicropropagationBiologyBotanyIn vitroHorticultureTissue culture

Abstract

fetched live from OpenAlex

Entire plants were regenerated from nodes explants of Jatropha curcas L. following a procedure of bud aggregate induction on MS (Murashige and Skoog) medium supplemented with 25 mg.l-1 citric acid, 12.2 mg.l-1 adenine sulfate, 15 mg.l-1 L-arginine, 2.46 µM IBA (indole-3-butyric acid), 30 g.l-1 sucrose and 7 g.l-1 of agar, and enriched with different balances of BA (benzyladenine) and L-glutamine. The histological studies performed on aggregates showed that the buds result from both the development of axillary buds and adventitious budding starting from underlying tissues of the explant. The culture medium containing 6.65 µM BA and 25 mg.l‑1 L-glutamine gave the best results with an average of 64 buds per aggregate after three weeks for all accessions tested. The buds developed into shoots when placed in an MS medium supplemented with 2.21 µM BA, 5.70 µM IAA (indole-3-acetic acid) and 15 mg.l-1 L‑arginine. These shoots were isolated and then rooted in MS containing 2.46 µM of IBA, 2% sucrose and 0.7% agar. The entire process took 13 weeks with a 98% survival rate in terms of plantlets acclimatization. We obtained a multiplication rate of 13 buds per explant and per subculture which is the double of those obtained in other recent works based on the micropropagation of J. curcas from node explants. This protocol is economically more profitable.

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 score0.018
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.218
Teacher spread0.212 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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