Evidence of the Involvement of <i>Nematostoma parasiticum</i> in Herpotrichia Needle Browning of <i>Abies balsamea</i>
Bibliographic record
Abstract
Herpotrichia needle browning (HNB) is a disease that affects several species of fir trees in Europe and North America. HNB was first described by Hartig in 1884, who isolated a fungal pathogenic agent identified as responsible for the disease. This fungus was later named Herpotrichia parasitica but is currently named Nematostoma parasiticum. However, the identity of the pathogens causing HNB is regularly questioned and, to date, the true causal agent of this disease has not been definitely established. The present study aimed to identify the fungal populations present in needles of Christmas fir trees (Abies balsamea) and to correlate them with needle health status using robust molecular methods. PCR primers specific to N. parasiticum allowed detection of the presence of this fungus in DNA samples from symptomatic needles. Furthermore, high-throughput sequencing (Illumina MiSeq) clearly showed that N. parasiticum was associated with symptomatic needles. However, high-throughput sequencing results revealed that the presence of other species such as Sydowia polyspora and Rhizoctonia sp. may also correlate with the development of HNB. A diagnostic tool, based on quantitative PCR using a probe, was then developed to detect and quantify N. parasiticum in DNA samples. The efficacy of this molecular approach was validated through the detection of the pathogenic agent in symptomatic needle samples as well as in nonsymptomatic needles collected in trees affected by HNB. In contrast, N. parasiticum could not be found in needles from healthy trees. The present study argues for the importance of N. parasiticum in causing HNB symptoms.
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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 | 0.000 |
| 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".