Examining the Impact of Winter and Spring Soil Temperatures on the Growth of Hypholoma fasciculare, a Potential Biocontrol Agent Against Armillaria ostoyae, in Pine Plantations
Bibliographic record
Abstract
Hypholoma fasciculareis regarded as a potentially effective biocontrol agent againstArmillariaroot disease. However, trials examining its effectiveness are currently limited to controlled lab conditions and field studies conducted mostly during the summer season. We examined the ability ofH. fasciculareto survive and grow underground during the winter and spring seasons to offer insight on its ability to provide year-round protection. Pine blocks inoculated withH. fascicularewere buried in three thinned pine plantations at 30 and 100 cm depths from February 1, 2018 to May 13, 2018 (101 days) to examine how winter and spring soil temperatures at two different soil depths impacted growth. A significant interactive effect of soil depth and month on soil temperature (F3,40= 15.94,p< 0.001) was observed. Mean growth rates did not vary significantly between the two soil depths (F1,23= 0.91,p= 0.393) as growth rates were 0.25 ± 0.11 and 0.31 ± 0.10 mm/day at 30 and 100 cm depths, respectively. Our study supports developingH. fasciculareas a biocontrol agent againstArmillariaroot disease given its ability to grow underground during the winter and early spring seasons, a period during whichArmillariahas a reduced growing capacity.
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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".