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Record W2957933508 · doi:10.5539/ijb.v11n4p26

Influence of Additional Nutrients and Gelling Agents on in Vitro Response of Selected Indica Rice Varieties

2019· article· en· W2957933508 on OpenAlexvenueno aff
Sai Krishna Repalli, Chaitanya Kumar Geda, Neera Pradhan, G. J. N. Rao

Bibliographic record

VenueInternational Journal of Biology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsnot available
FundersIndian Council of Agricultural ResearchNational Rice Research Institute, Indian Council of Agricultural Research
KeywordsCallusNutrientProlineBiologyIn vitroTissue cultureBiotechnologyHorticultureAgronomyBotanyFood scienceBiochemistryAmino acid

Abstract

fetched live from OpenAlex

Indica rice varieties are recalcitrant to culture and hence the culture media should be supplemented with additional nutrients to provide energy and osmotic potential for best in vitro response. Combinations of plant growth regulators have profound influence on callus induction and regeneration potential of the selected genotypes. In addition, concentration and choice of gelling agents also have their effect on regeneration of indica rice varieties. Impact of L-Proline, and Casein Hydrolysate on tissue culture response of selected indica rice varieties is discussed and the best choice of gelling agent and their in vitro response is elucidated.

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 score0.095
Threshold uncertainty score0.171

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 head0.006
GPT teacher head0.252
Teacher spread0.245 · 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

Citations10
Published2019
Admission routes1
Has abstractyes

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