Salinity-Induced Alterations in the Growth, Membranes and Osmolytes of Musa spp. cv. Caipira (AAA) During Micropropagation
Bibliographic record
Abstract
The aim of this study was to determine the concentration and exposure time to NaCl suitable for the micropropagation of banana, through the analysis of growth traits. Banana propagules were inoculated in MS medium with different concentrations of NaCl (0; 50; 75 and 100 mM) for 120 days (multiplication and rooting, 60 days each), with monthly subcultures. These propagules were measured for plant height, number of leaves, sprouting rate, average number of formed propagules, rooting rate, root length and survival rate. After 30 days, NaCl reduced sprouting rate at multiplication; the number of leaves, rooting rate and root length in rooting; and the height and propagules number in both phases. After 60 days, the NaCl affected the sprouting rate and propagules number in the multiplication; length of root in rooting; and the height and number of leaves in both phases. After 120 days, the reduction in the survival rate was proportional to the increase of NaCl in the medium. Thus, it is concluded that NaCl reduces most of the growth traits and the treatments with 75 and 100 mM NaCl affected multiplication and in vitro rooting more intensely.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".