Developing a scale-up system for the in vitro multiplication of thidiazuron-induced strawberry shoots using a bioreactor
Bibliographic record
Abstract
The use of large-scale liquid cultures in a bioreactor system has the potential to resolve the manual handling of the various stages of micropropagation and increases shoot multiplication in vitro significantly compared with those cultured on semi-solid gelled medium. In an attempt to improve the micropropagation protocol for strawberry (Fragaria × ananassa Duch.), a procedure for the mass propagation of adventitious shoots regenerated from leaf, sepal and petiole explants of cultivar Bounty using a liquid medium-containing bioreactor system combined with gelled medium is described. Leaf disks, sepals and petiole halves produced multiple buds and shoots without an intermediary callus phase on 2-4 µM thidiazuron (TDZ)-containing shoot induction medium within 5-6 wk of culture initiation. TDZ supported rapid shoot proliferation at low concentrations (0.1 µM), but induced hyperhydricity in a bioreactor system. Bioreactor-multiplied hyperhydric shoots were transferred to gelled medium containing 2-4 µM zeatin, and produced normal shoots and root within 4 wk of culture. In vitro derived plantlets were acclimatized and eventually established in the greenhouse and in the field. Present results suggested the possibility of large-scale multiplication of strawberry shoots in bioreactors. Key words: Fragaria × ananassa, growth regulator, shoot regeneration, RITA® bioreactor
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".