Detection of cranberry fruit rot fungi using DNA array hybridization
Bibliographic record
Abstract
A PCR-based DNA macroarray hybridization technique (also called reverse dot blot hybridization) was developed for cranberry fruit rot (CFR) fungal pathogens, and its detection capability was compared with that of the traditional isolation plating method for CFR isolation and identification from over 2000 field samples. DNA array hybridization results correlated well with detection by isolation when cranberry fruit samples had calyces removed. It also provided detection of CFR fungi not recovered by isolation. When calyces were not removed, the number of cranberry samples where a species was isolated but not detected on the array increased. Isolation without array detection was also correlated with using greater amounts of berry mass for DNA extraction. This was due to the complexity of DNA template mixtures and the presence of some fungal species at very low concentrations. Multiple PCR reactions may be necessary to accurately detect the diversity of fungal pathogens in such situations. Overall, the use of DNA array hybridization for CFR fungi detection is a rapid, sensitive, and cost-effective technique that shows great potential for future CFR research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".