Effect of Mesos Components (MgSO4, CaCl2, KH2PO4) on In Vitro Shoot Growth of Blackberry, Blueberry, and Saskatoon
Bibliographic record
Abstract
Berry fruit species are, in many countries, considered biologically and economically valuable and important species of small fruits. The aim of this work was to examine the influence of either decreased or increased mesos concentrations (MgSO4, CaCl2, and KH2PO4) on shoot multiplication of five cultivars of three small fruit species (Amelanchier alnifolia var. cusickii, Rubus fruticosus ‘Black Satin’ and ‘Loch Ness’, and Vaccinium corymbosum ‘Brigitta Blue’ and ‘Toro’). Mesos nutrients were manipulated from half to four times their base concentration. The results indicate that mesos manipulation significantly influences the number and length of shoots in most of the studied cultivars. The greatest multiplication rate for A. alnifolia was achieved with tripled mesos, whereas ‘Black Satin’ and ‘Loch Ness’ reacted positively to a lower (1–2x) concentration of mesos. Decreasing the concentration of mesos to half led to worse quality in both blackberry and Saskatoon shoots. ‘Brigitta Blue’ was more sensitive to greater mesos concentrations compared to ‘Toro’. Optimizing the mineral nutrition of plants cultivated in vitro enhances their multiplication rate and contributes to a higher production of good quality plantlets.
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 | 0.000 |
| 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".