Bibliographic record
Abstract
While strawberries have long enjoyed huge popularity among consumers, tremendous progress in plant tissue culture, resulting in great advances in micropropagation, has occurred. The in vitro morphogenesis seems to be highly dependent on plant growth regulators and media used for culture, which is again genotype specific. Although automation of micropropagation in bioreactors has been advanced as a possible way of reducing propagation cost, optimal plant production depends upon a better understanding of physiological and biochemical responses of plants to the signals of culture microenvironment and an optimization of specific physical and chemical culture conditions to control the morphogenesis of strawberry 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. The article describes the progress in-depth of various aspects of strawberry propagation in vitro on semi-solid gelled media and in liquid media using bioreactors, for their improvement and for commercial production. The article also focuses on the 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 regenerant.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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 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".