Occurrence of cucurbit viruses on field-grown melon and watermelon in the Thrace region of Turkey
Bibliographic record
Abstract
A survey for the detection of cucumber mosaic virus (CMV), papaya ringspot virus-W (PRSV-W), squash mosaic virus (SqMV), melon necrotic spot virus (MNSV), cucumber green mottle mosaic virus (CGMMV), zucchini yellow mosaic virus (ZYMV) and watermelon mosaic virus-2 (WMV-2) was carried out in June and July 2005, covering 17 melon fields and 19 watermelon fields in the Tekirdag, Edirne and Kırklareli provinces of Turkish Thrace. Among all the fields sampled, only one melon field was not virus-infected. In all, 502 melon and watermelon samples were tested for the presence of seven viruses with ELISA tests using polyclonal antisera. Overall, 333 out of 502 samples tested positive for the investigated viruses: 167 out of 235 plant samples in Tekirdag, 103 out of 187 samples in Edirne, and 63 out of 80 samples in Kırklareli were positive. Serological tests showed that six out of the seven tested viruses were present in the Thrace region of Turkey. The following rates of incidence of tested viruses on watermelon were found: ZYMV (45.5%), WMV-2 (34.2%), CMV (19.9%), PRSV-W (2.1%), SqMV (1.8%) and MNSV (0.4%), while the rates of incidence on melon were ZYMV (40.3%), WMV-2 (31.2%), CMV (7.2%), PRSV-W (2.3%), SqMV (0.5%) and MNSV (1.8%). The WMV-2+ZYMV mixed infection type was the most widespread both on melon and on watermelon samples at 16.7% and 11.4%, respectively.
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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 | 0.000 |
| 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".