Effect of leaf scar age, chilling and freezing-thawing on infection of <i>Pseudomonas syringae</i> pv. <i>syringae</i> through leaf scars and lenticels in stone fruits
Bibliographic record
Abstract
Introduction . Bacterial canker, caused by P. syringae pv. syringae , is an important disease of stone fruit worldwide. The possibility of P. syringae pv. syringae infection through leaf scars and lenticels was evaluated in cherry, peach and prune. Materials and methods . Laboratory and field inoculations were performed using cherry, peach and prune stems to evaluate leaf scar age, chilling and freezing-thawing on bacterial infection through leaf scars and lenticels. Results and discussion . Increasing leaf scar age was associated with significant decreases in disease incidence and length of lesions resulting from leaf scar inoculation with Pseudomonas syringae pv. syringae in cherry, peach and prune. A significant reduction in incidence and lesion length was observed after 4 h of air exposure, and both measures of infection were reduced to essentially 0 by 2 days of exposure. Prolonged chilling temperature (2.2 °C) prior to leaf removal had no clear effect on disease incidence of leaf scar infection, but significantly decreased lesion length due to leaf scar infection. Cherry was more susceptible to P. syringae pv. syringae infection through leaf scars than peach and ‘French’ prune. The leaf scar inoculation results were consistent with the previous studies. The disease incidence of lenticel infection caused by bacterial inoculation in ‘French’ prune was very low, but significantly higher than the water control. Freezing-thawing significantly increased both the disease incidence and the lesion size via lenticel infection. The lenticel inoculation data suggest that P. syringae pv. syringae infection through lenticels is possible under field conditions.
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 | 0.001 |
| 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".