Influence of mineral nutrients and freezing-thawing on peach susceptibility to bacterial canker caused by <i>Pseudomonas syringae</i> pv. <i>syringae</i>
Bibliographic record
Abstract
Introduction. Bacterial canker, caused by Pseudomonas syringae pv. syringae ,i s a devastating disease of stone fruit worldwide. The effects of mineral nutrients and freezing-thawing on bacterial canker susceptibility were evaluated using potted peach trees in an attempt to understand predisposing factors in bacterial canker of stone fruit. Materials and methods. A split-plot experi- mental design with randomized complete block main plots (i.e.,inoculations associated with freezing- thawing or nonfrozen pretreatments) and subplots of trees with the seven treatments (i.e., solutions deficient in N, P, K, Ca, Mg or Fe, respectively, and a full nutrient control) was adopted to study the effect of mineral deficiency and freezing-thawing on peach susceptibility to bacterial canker. Results anddiscussion. Phosphorus deficiency was the only treatment to significantly decrease lesion length that developed after inoculation with P. syringae pv. syringae, compared with the control trees that received full nutrients. Nitrogen and potassium deficiency treatments significantly decreased bark nitrogenandpotassiumconcentrationsaccordingly,buthadnocleareffectonlesionsizes.Inoculation during freezing-thawing cycles significantly increased lesion length. In another independent expe- riment, nitrogen deficiency significantly increased the number of P. syringae pv. syringae leaf scar infections, but the subsequent infection was limited to a few millimeters. Nitrogen-deficient trees, which had higher (carbon / nitrogen) ratios, developed lesion sizes equivalent to trees provided with full nutrients. Collectively, these data suggest that, in the absence of other major predisposing factors (i.e., low soil pH or ring nematodes), mineral nutrients may play a minor role in the susceptibility of peach to bacterial canker.
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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 | 0.000 | 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".