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Record W2993430313 · doi:10.1002/elsc.201900133

Improved in vitro rooting in liquid culture using a two piece scaffold system

2019· article· en· W2993430313 on OpenAlexafffund
Mukund R. Shukla, Kevin Piunno, Praveen K. Saxena, Andrew Maxwell Phineas Jones

Bibliographic record

VenueEngineering in Life Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsUniversity of Guelph
FundersMinistry of Agriculture, Food and Rural AffairsOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsMicropropagationLiquid cultureScaffoldLiquid mediumPlant tissue cultureAgricultural engineeringTissue cultureProcess (computing)BotanyHorticultureBiologyComputer scienceBiochemical engineeringProcess engineeringPulp and paper industryIn vitroBiomedical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Plant tissue culture techniques have been used to propagate horticultural crops at a commercial scale for more than three decades. However, due to the high cost it is generally only used for high value crops. To increase production efficiency and make micropropagation viable for a wider range of species, new approaches to address key steps of the process with high labor inputs need to be evaluated. For this study, a two‐piece scaffold system was designed, prototyped using 3D printing, and tested to physically hold plants upright thereby facilitating liquid based rooting. This system was evaluated with Malus domestica , Betula lenta , and Musa sp. using static liquid culture as well as rocker based temporary immersion system and compared to rooting in semi‐solid based medium as is commonly practiced. Significantly, earlier rooting was observed in all three species in liquid when compared to semi‐solid culture system, and plants cultured in liquid on the rocker generally performed better than those in static liquid. In addition to quicker, more uniform rooting, reducing labor requirements, and preventing root damage. This newly designed system is simple, easy to use, will help to improve efficiency, and reduce the cost of micropropagation.

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 score0.203
Threshold uncertainty score0.328

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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations26
Published2019
Admission routes2
Has abstractyes

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