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Record W3012073282 · doi:10.1139/cjb-2019-0206

In vitro rooting of hybrid hazelnuts (<i>Corylus avellana</i> × <i>Corylus americana</i>) in a temporary immersion system

2020· article· en· W3012073282 on OpenAlexaffvenue
James Nicholson, Mukund R. Shukla, Praveen K. Saxena

Bibliographic record

VenueBotany · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant tissue culture and regeneration
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPlantletExplant cultureBiologyMicropropagationAcclimatizationShootBotanyHorticultureSowingIn vitro

Abstract

fetched live from OpenAlex

Commercial micropropagation of hybrid hazelnuts (Corylus avellana L. × C. americana Marshall) has been limited, owing to their poor rooting ability in vitro as well as ex vitro, leading to high mortality of plantlets transplanted in the greenhouse. The objective of this study was to develop an efficient and cost-effective protocol for rooting and plantlet acclimation of in vitro grown hazelnut shoots. Efficient in vitro rooting was accomplished in a rocker-based temporary immersion bioreactor system. The use of a temporary immersion system (TIS) in combination with the inert substrate Oasis® In Vitro Express (IVE) significantly improved the in vitro rooting efficiency (100%) compared with semi-solid medium (27%) after four weeks of culture. A higher density (36 explants/vessel) of shoot explants in the TIS was found to support a significantly greater shoot height, chlorophyll content, and longest root length, compared with the lowest density treatment (12 explants/vessel). Efficiency of rooting and the number of roots formed were similar for both the high and low density of explants in the culture vessels, and the resulting plantlets exhibited &gt; 80% survival in the greenhouse. These results demonstrate the usefulness of rocker-based TIS for commercial micropropagation of hazelnuts and, potentially, other tree species.

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 score0.005
Threshold uncertainty score0.547

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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
Published2020
Admission routes2
Has abstractyes

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