A Method to Induce Stem Cankers by Inoculating Nonwounded <i>Populus</i> Clones with <i>Septoria musiva</i> Spore Suspensions
Bibliographic record
Abstract
Most artificial inoculations of Populus spp. stems with Septoria musiva have required host wounding to induce canker development; in the absence of wounds, frequencies of cankers have been low. Three greenhouse inoculation experiments were conducted to demonstrate the reliability and repeatability of an inoculation method that did not require wounding. In the first, 16 clones of hybrid poplar were inoculated with three isolates of S. musiva to compare responses following wounding and inoculation with mycelium (wound inoculation) with responses following inoculation of nonwounded trees by spraying with a conidial suspension (spray inoculation). Stem disease severity among clones following spray inoculation was correlated with stem disease severity following wound inoculation. A significant clone-isolate interaction was detected with spray inoculation but not wound inoculation. In the other two greenhouse experiments, 29 clones of hybrid poplar and 69 clones of Populus balsamifera were inoculated with a spore suspension mixture of three isolates. In both cases, the experimental error was similar to that obtained in previous experiments, in which trees were wound inoculated, and was adequately small to permit detection of differences in responses among clones. Ultimately, field studies will be needed to determine the best inoculation method for predicting stem responses to this pathogen under field conditions. However, relative to wound inoculation, spray inoculation of nonwounded trees has the advantage of yielding faster results, permitting inoculation with a mixture of isolates, and not circumventing potential mechanisms for resisting penetration. The ability to infect stems without wounding creates opportunities for numerous types of epidemiological and disease control studies that are difficult to conduct with wound inoculation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".