Different Methods for Overcoming Integumental Dormancy during in vitro Germination of Red Araza Seeds
Bibliographic record
Abstract
Red Araza, or Red Strawberry Guava (Psidium cattleianum Sabine) is a native Brazilian Atlantic Forest species of the Myrtaceae family, whose seeds exhibit integumental dormancy. Due to its importance to different industries worldwide, recent research efforts are seeking to expand this species’ micropropagation processes using in vitro seedling germination, especially since in vitro micropropagation of adult plant material has, so far, been limited. This research effort evaluated different methods of overcoming integumental dormancy during in vitro germination of the Red Araza, so as to allow future micropropagation of the species. The seeds’ emergence and vigor were evaluated based on mechanical and acid scarification, using different substrates and immersions in solutions with different levels of gibberellic acid (GA3), and on the influence of the pre-immersion of seeds in water and sulfuric acid. The mechanical and acid scarification of the seeds, combined or separate, resulted in higher in vitro germination percentages and a higher germination rate index (GRI). Pre-immersion in distilled water (20 hours) also proved to be efficient for the germination of the Red Araza seed, with 76.2% of the seeds germinating and a higher speed of emergence (GRI = 0.18). When compared to a Murashige and Skoog (MS-zero) medium, sowing in a hydrophilic cotton substrate showed greater emergence and vigor, with approximately 70% of the seeds germinating. Treating the seeds by pre-immersing them in GA3 turned out to be unnecessary. The methods used for overcoming integumental dormancy during in vitro germination of Red Araza seeds proved to be efficient, and could be used to develop micropropagation protocols of seminal origin for this species.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".