Health and Quality of Wheat Seed Samples Collected from Sadar Upazilla of Thakurgaon District and Control of Seed-Borne Fungi
Bibliographic record
Abstract
Wheat seed samples were collected from ten unions of sadar upazilla of Thakurgaon district in wheat growing season of 2011. Seeds were tested by blotter method at Seed Pathology Center (SPC), Bangladesh Agricultural University, Mymensingh during the months April to November 2011 for recording and identifying the seed-borne fungi associated with wheat seeds. The health status of 20 seed samples were determined whereas five genera fungi were identified from a total of six fungus. The fungi were Bipolaris sorokiniana, Alternaria tenuis, Fusarium spp, Penicillium sp, Aspergillus flavus and Aspergillus niger. Prevalence of the total as well as the individual seed-borne fungal infections that were recorded varied significantly with respect to wheat varieties and sources of seed collection. Seed samples collected from Jagonathpur and Gorea unions of sadar upazilla showed highest percentage of seed-borne infection compared to the samples collection from other unions for both varieties. The seed-borne fungal infection in Hazar-8 and Satabdi collected from other unions showed lowest percentage of seed-borne fungal infection than that of Jagonathpur and Gorea unions. Seed germination also varied significantly depending on the varieties and the seed sources and a positive correlation between seed germination and seed-borne fungal infections were observed. Three seed treating agents viz., neem leaf extract 1:2, hot water and Provax were evaluated for controlling seed-borne fungi associated with wheat seeds. Among the seed treating agents, Provax was found superior to reduce the seed-borne infection of wheat. The results also showed that neem leaf extract at 1:2 dilutions was observed to be the most effective in reducing seed-borne fungi with highest percentage of germination followed by hot water treatment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".