Identification and Characterization of Bacterial Agents Causing Moderate Damage and Souring of the Fig Fruits
Bibliographic record
Abstract
In order to determine the factors of decay and sourness of fig fruits, in the summer of 2016-17, 60 leaf, fruit and stem samples from different regions of Tehran, Varamin (Qal'e No), Mazandaran (Amol, Noor and Sari), Lorestan from Iran and a branch of fig fruit sample from Italy were collected. We obtained 30 isolates from the sample. The pathogenicity of 30 isolates were confirmed by artificially inoculation using fig fruits. They were also characterized based on key phenotypic traits. All 30 isolates showed hypersensitivity reaction to tobacco, pelargonium and did not show pathogenicity to potato tubers. 16S rRNA gene of the 10 representative isolates were sequenced. Ten isolates were identified as Stenotrophomonas maltophilia, Pseudomonas aeruginosa, Pseudomonas fulva, Brevibacterium linens, Pseudomonas fragi, Bacillus licheniformis, Bacillus paralicheniformis and Bacillus cereus based on the determined sequences. None of the isolates caused fruit rot but typical disease symptoms were observed on fig leaves and fruits. This is the first report of the presence of pathogenic bacteria on fig trees in Iran.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".