Bioreactors and molecular analysis in berry crop micropropagation – A review
Bibliographic record
Abstract
Debnath, S. C. 2011. Bioreactors and molecular analysis in berry crop micropropagation – A review. Can. J. Plant Sci. 91: 147–157. While berry fruits have long enjoyed huge popularity among consumers, tremendous progress in plant tissue culture, resulting in great advances in micropropagation, has occurred. Of particular significance has been the evolution of the technology permitting multiplication of berry plants in bioreactors containing liquid media. Although automation of micropropagation in bioreactors has been advanced as a possible way of reducing propagation cost, optimal plant production depends upon better understanding of physiological and biochemical responses of plant to the signals of culture microenvironment and an optimization of specific physical and chemical culture conditions to control the morphogenesis of berry plants in liquid culture systems. Clonal fidelity can be a serious problem, and molecular strategies have been developed in order to reduce the variation to manageable levels. Molecular markers have been introduced to tissue culture research and can potentially be used in various facets of pertinent studies with berry crops. The paper focuses on bioreactor systems combined with semi-solid media used for in vitro culture of berry crops, cultivation of micropropagules and employment of molecular markers in micropropagated plants for the assessment of genetic fidelity, uniformity, stability and trueness-to-type among donor plants and tissue culture regenerants. The pertinent literature is reviewed and the relative merits and shortcomings of the various molecular markers applied are presented with an emphasis on the nature of tissue culture-induced variation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".