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Record W3027265216 · doi:10.3390/agronomy10050744

In Vitro Propagation Strategies of Medicinally Important Berry Crop, Lingonberry (Vaccinium vitis-idaea L.)

2020· article· en· W3027265216 on OpenAlexafffund
Samir C. Debnath, Usha Arigundam

Bibliographic record

VenueAgronomy · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsMemorial University of NewfoundlandAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsMicropropagationVacciniumBiologyShootVegetative reproductionBotanyPlant propagationIn vitroExplant culture

Abstract

fetched live from OpenAlex

Lingonberry (Vaccinium vitis-idaea L.) is a health-promoting small fruit crop rich in antioxidant metabolites that helps to reduce the incidence of degenerative diseases. Being heterozygous, lingonberries cannot preserve genetic characteristics through seed propagation. Conventional vegetative propagation, although it produces true-to-type plants, is not economically viable. In vitro propagation can multiply plants much faster than conventional methods. A liquid cultures system under a bioreactor micropropagation system is of significant importance to increase the multiplication rates of in vitro-produced shoots. Enhanced vegetative growth and variation in biochemical constituents are observed in micropropagated plants. Clonal fidelity, although it may be a serious problem for commercial micropropagation, can be verified efficiently by molecular markers. The current review provides detailed and updated information on lingonberry micropropagation along with conventional methods and their effects on morphological, molecular and biochemical characteristics in micropropagated plants, filling the gap in literature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.231
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations52
Published2020
Admission routes2
Has abstractyes

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