‘Effective Micro-organisms’ (EM): An Effective Plant Strengthening Agent for Tomatoes in Protected Cultivation
Bibliographic record
Abstract
The effect of treating organically grown tomato plants with Effective Micro-organisms (EM) combined with a stone dust-suspension (EM treatment) was tested in a pot experiment in a foliar tunnel at the University of Natural Resources and Life Sciences Vienna. In the EM treatment, the irrigation water was amended with EMa® and plants were treated with EM-stone dust-suspension. In the control treatment, tap water was used instead. In the EM treatment, bokashi, wheat bran fermented with EMa®, was additionally added to the planting substrate in both years. Only in 2007, the equivalent amount of wheat bran, composted without EMa® addition, was added to the substrate in the control as well. Inorganic N contents of the substrate were lower in the control in 2006, but increased when wheat bran compost was added in 2007. N mineralization at later stages of the experiment was higher in the EM treatment in 2007. Microbial biomass in the substrate was enhanced in both years. Total yield was higher and the number of fruits damaged by blossom-end rot was reduced in the EM-treated plants in 2007. The percentage of fruits in the best quality class was significantly higher in the EM treatment in both years. N, P and K contents in tomato leaves of the EM treatment were reduced, whereas the Fe content was higher. A more even N supply to the plants in the EM treatment, combined with the effect of a direct stone dust-application onto the plants, clearly increased plant yield and fostered plant health.
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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) | 0.000 | 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".