New Detection Method for Fungal Infection in Silver Fir Seeds
Bibliographic record
Abstract
Silver fir trees have cycles of low and high seed production, and thus it is necessary to collect seeds in high production years to save them for low production years to ensure the continuity of nursery production. Tree seeds can be stored loosely in piles or containers, but they need to be checked for viability before planting. The objective of this study was to find a quick and inexpensive method to determine the suitability of seed lots for planting. The working hypothesis was that an electronic nose device could be used to detect odors from fungi or from decomposing organic material, and thus aid in determination of whether seeds could be sown or discarded. To affirm and supplement results from the electronic nose, we used gas chromatography–mass spectrometry (GC-MS) to detect volatile secondary metabolites such as limonene and cadienes, which were found at the highest concentrations in both, infected and uninfected seeds. Uninfected seeds contained exceptionally high concentrations of pinene, which are known to be involved in plant resistance responses. Statistically higher levels of terpineol were found in infected seeds than in uninfected seeds. A prototype of our electronic nose partially discriminated between healthy and spoiled seeds, and between green and white fungal colonies grown on incubated seeds. These preliminary observations were encouraging and we plan to develop a practical device that will be useful for forestry and horticulture.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".