Inventory of Fungi on Imported Wheat Grains from Canada at the Large Agricultural Quarantine Center Surabaya
Bibliographic record
Abstract
Wheat is one of the most popular commodities and is often consumed by the Indonesian people. In 2019, wheat was consumed at least 30.5 kg/year by the Indonesian people. The import of wheat in large quantities allows the risk of carrying OPTK, especially fungi with OPTK category A1, so detection and identification of wheat seeds entering Indonesia is needed. This study aims to detect and determine the fungi found in wheat seeds from Canada (Canada I and Canada II) using the Washing Test method at the Surabaya Agricultural Quarantine Center. The Washing test method is a washing method to release fungi on the surface of seeds using sterile distilled water and a centrifuge. The stages of this method are as follows sampling of wheat seeds from Canada, detection using the washing test method, and microscopic identification. The results of detection and identification on wheat seeds did not find the target pest but other fungi were found in Canada I sample, namely Cladosporium variabile, Cladosporium sp., Puccinia sp., Puccinia graminis and in Canada II sample Cladosporium variabile, and Puccinia graminis. Further quarantine measures for imported wheat grains are exemptions with the issuance of a plant exemption certificate (KT-9). Service procedures at the BBKP Surabaya laboratory are carried out in accordance with Law No. 21 of 2019, MOA No. 25 of 2020 and ISO 17025 with 2P quarantine measures (Inspection and Exemption).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".