Birch foliar responses to simulated acidic fog and<i>Septoria betulae</i>inoculations
Bibliographic record
Abstract
The effects of simulated acidic fog and inoculation with Septoria betulae Pass. on foliar symptom development and foliar senescence of Betula papyrifera Marsh. and Betula cordifolia Regel seedlings were investigated in 1997 and 1998 under greenhouse conditions. An interactive role may exist between acidic fog events and S. betulae in causing birch foliar browning, a disease reported over the past decade to occur on mature trees growing adjacent to the Bay of Fundy, Canada. Seedlings received applications of simulated fog adjusted to pH 3.2, 4.2, and 5.6 or a no-fog treatment. Inoculation treatments at each fog pH level were accomplished through spray atomization with S. betulae conidial suspensions and by placing naturally infected birch leaves bearing pycnidia of the fungus on plastic nets suspended above seedlings in enclosed chambers. Percent symptomatic leaf area of seedlings inoculated with S. betulae was nearly double that recorded for non-inoculated seedlings. Foliar browning resembled symptoms observed on mature trees in the field. Foliar symptoms were observed on non-inoculated seedlings, with greater severities associated with seedlings exposed to the most acidic fog treatment. Leaf senescence was also greatest for spray-inoculated leaves that had been exposed to the pH 3.2 fog treatment. Pycnidial development was not influenced by the pH of the fog treatments but was greater in all fog treatments than in no-fog treatments. Although both acidic fog and S. betulae infection are able to cause symptoms independently, the data suggest that an interactive role may exist in causing birch foliar browning. However, to obtain the same severity of foliar browning as observed on natural forest-grown trees in the Bay of Fundy region, S. betulae must be present.
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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".