Evaluating mechanical wounding as a surrogate for aspen shoot blight caused by<i>Pollaccia americana</i>in the assessment of crop loss
Bibliographic record
Abstract
It is difficult to maintain, in trembling aspen (Populus tremuloides), controlled levels of aspen shoot blight (ASB) caused by Pollaccia americana, under field conditions. Thus, simulating the disease by mechanical wounding may be useful in crop-loss assessment. Three treatments were compared to evaluate the suitability of decapitation as a surrogate for ASB: (1) artificial inoculation with P. americana causing ASB and (2) cutting of the shoot between the fifth and sixth expanded leaves (decapitation), both these treatments being later referred to as injury treatments, and (3) no injury (control). The three treatments were applied to trees from four different seed lots, which subsequently grew under field conditions, and to two different clones held in a growth chamber at soil temperatures of 6, 12, or 20 °C. Leader length, frequency of axillary-shoot development, and lateral- and axillary-shoot lengths were evaluated. Trees infected with ASB and trees that were decapitated responded very similarly and had significantly longer leaders and more axillary shoots than did control trees. Although there were significant differences in branching patterns among seed lots, and significant clone and temperature effects on infection, the effects of treatments were typically consistent across all levels of clone, seed lot, and temperature. These results indicate that decapitation could be used to simulate ASB in assessing crop loss.
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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".