Effect of biostimulants on mustard microgreens grown under different cultivation conditions
Bibliographic record
Abstract
Ten products, commercially available as biostimulants or reported for their biostimulating properties, were tested under conventional and organic growing systems for their effects on mustard microgreens (Brassica juncea) grown in presence of abiotic (salt) or biotic (Pythium ultimum) stress. Drench application of wollastonite (calcium silicate) significantly improved the germination rate of mustard seeds sown in a substrate inoculated with P. ultimum in conventional growing system exclusively. In both growing systems, no significant effect of biostimulants was observed on the dry biomass or the proportion of healthy microgreens grown in presence of P. ultimum. None of the biostimulants significantly increased the germination rate of seeds exposed to a salinity stress in both growing systems while humic acid, triacontanol, chitosan, and Bacillus subtilis PTB185 significantly decreased the germination rate of seeds exposed to 40, 80 or 120 mM NaCl L-1 under conventional or organic management. Seed treatment with Trichoderma harzianum T-22 and humic acid resulted in microgreens with a significantly higher dry biomass when subjected to 40 and 80 mM NaCl L-1 under conventional and organic management, respectively. The study showed that the effects of the biostimulants vary from beneficial to detrimental and that plant response to biostimulants is influenced by the cultivation conditions.
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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 | 0.000 |
| 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".