Biological features and conditions of surface cultivating of a strain-producer of microbiopreparation Т-1 Trichoderma sp. against pathogen causing fusarium on oil flax
Bibliographic record
Abstract
To develop technological regimen for production of microbiopreparations in a preparation form ‘wetting powder’ we studied biological features and conditions of surface cultivating of a strain-producer Т-1 Trichoderma sp. – an antagonist of pathogen Fusarium oxysporum Schlecht. emend. Shyd. et Hans. var. orthoceras (App. еt Wr.) Bilai and Fusarium poae (Peck) Wollenw., Lewis on oil flax. To study cultural and physiological qualities of the strainproducer we used agar and liquid mediums. Surface cultivation of a fungus on agar and liquid Rudakov’s medium at a temperature 25–30 оС was the most favorable for mycelium growth and spore formation. Stationary fungus cultivation on liquid medium with рН from 3 to 6 provided maximal mycelium growth with spore formation and the highest dry mass. Addition of starch into the Chapek’s nutrient medium caused maximal growth of fungus mycelium and increase of its dry mass. The best source of nitrogen for a fungus strain was corn extract. Rudakov’s and No1 mediums are optimal compound liquid nutrient mediums for a surface cultivation of the strain-producer. Optimal period of the surface cultivation of the fungal strain Т-1 Trichoderma sp. on liquid Rudakov’s nutrient medium was 10 days, and a volume of sowing culture to a nutrient medium – 2.0%.
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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".