Liquid chromatography/mass spectrometry for the detection of ash tree metabolites following Emerald Ash Borer infestation
Bibliographic record
Abstract
RATIONALE: The Emerald Ash Borer (EAB), Agrilus planipennis, an invasive insect detected in the USA and Canada in 2002, is a threat to ash trees with both ecological and economic implications. Early detection of EAB-infestation is difficult due to lack of visible signs and symptoms in the early stages of attack, but is essential to prevent ash mortality. An efficient and reliable tool for the early detection of EAB-infestation would be advantageous. METHODS: A mass spectrometry based metabolomics approach, using liquid chromatography/mass spectrometry (LC/MS), has been used to investigate the leaf metabolites of both healthy and EAB-infested trees. RESULTS: Leaves from 40 healthy and 40 EAB-infested trees were extracted and analyzed using LC/MS. Resulting data were examined to differentiate between foliage from healthy and EAB-infested trees. Possible biomarkers of EAB attack have been detected. Twenty-one metabolites with increased average ion intensity in EAB-infested ash tree samples and nine metabolites with increased average ion intensity in healthy ash tree samples were identified. CONCLUSIONS: Results of this study indicate that metabolomic screening of leaf samples using LC/MS can be useful as a potential tool for the early detection of EAB-infestation.
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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 | 0.001 |
| 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".