Influence of mycorrhizal fungi on the growth parameters of different cherry cultivars in the Kyustendil region
Bibliographic record
Abstract
The influence of arbuscular mycorrhizal fungi in the EC fertilizer Dynocarb MYC on the growth of 16 newly introduced cherry cultivars for the Kyustendil region - Primulat, Early Lorry, Sweet Lorry, Lorry Bloom, Big Lorry, Giant Red, Firm Red, Giorgia, Folfer, Early Star, Simcoe, Canada Giant, Skeena, Katalin, Sweetheart, Ferrovia, Bigarreau Burlat and Van was monitored. It was established that applying Dynocarb MYC positively affected the growth properties of the trees in all investigated cultivars. Regarding trunk diameter (cm²), differences compared to controls were positively demonstrated in Skeena, Firm Red, Early Star, Giant Red and Lorry Bloom. Растениевъдни науки, 2023, 60(5) Bulgarian Journal of Crop Science, 2023, 60(5) https://doi.org/10.61308/NYAB1154 77 For the other cultivars, the differences were insignificant, but the tendency was positive. The trees of the treated variants were proven to be higher in the cultivars Early Lorry, Firm Red, Early Star, Sweetheart, Ferrovia, Skeena, Katalin, Lorry Bloom, Bigarreau Burlat, and Van. In the treated cultivars, a greater average length of annual shoots was found in the cultivars Early Lorry, Firm Red, Early Star, Sweetheart, Ferrovia, Skeena, Katalin, Lorry Bloom, Bigarreau Burlat and Van.
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) | 0.000 | 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".