Antifungal Activities of Crude Extractum from Camellia semiserrata Chi (Nanshancha) Seed Cake Against Colletotrichum musae, Colletotrichum gloeosporioides and Penicillium italicum in vitro and in vivo Fruit Test
Bibliographic record
Abstract
Antifungal activities of crude extractum of Nanshancha Seed Cake (NSC), to inactivate postharvest pathogens were investigated. Highest inhibitory rate was found against C. musae, C. gloeosporioides and C. papaya P.Henn, which was much stronger than that by tea saponin. Compared to tea saponin, effects of NSC extractum was relatively weak and similar on C. gloeosporioides Penzig and P. italicum. In an in vivo study, best controlling effects by NSC extractum was found with banana anthracnose disease development, which showed no inhibitory effects by tea saponin. NSC extractum controlled in vitro C. musae growth through directly inhibiting germination rate and germ tube elongation, and causing distortation, rupture and indentation of C. musae mycelium. In banana fruit subject to C. musae inoculation, higher PAL, POD, GLU and CHT activity was observed in banana fruit treated with crude NSC extractum than that of water control fruits. Current study proved the best controlling effects of crude NSC extractum in C. musae in vitro and in vivo development, which through direct inhibition of C. musae growth and increasing defense system of the banana fruit.
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".