<b>Molecular recognition of emerald ash borer infestation using leaf spray mass spectrometry</b>
Bibliographic record
Abstract
RATIONALE: The introduction of the emerald ash borer (Agrilus planipennis) (EAB) from Asia to Michigan, USA, in the 1990s caused the widespread death of ash trees in two Canadian provinces and 24 US states. The three current methods for the detection of emerald ash borer infestation, visual surveys, tree girdling and artificial traps, can be unreliable, and there is clearly a need for a rapid, dependable technique for the detection of emerald ash borer infestation. METHODS: Leaf spray, an ambient ionization method for mass spectrometry (MS), gives direct chemical information on a leaf sample by applying a high voltage to a naturally or artificially sharply pointed leaf piece causing ions to be generated directly from the leaf tip for MS analysis. RESULTS: Leaflets from 23 healthy and EAB-infested ash trees were analyzed by leaf spray mass spectrometry in an attempt to distinguish healthy and EAB-infested ash trees. In negative ion mode, healthy ash trees showed an increased abundance of ions m/z 455.5, 471.5 and 487.5, and ash trees infested with the EAB displayed an increased abundance of ions m/z 181 and 217. The identities of the chemical discriminators ursolic acid and oleanolic acid in healthy ash trees, and six-carbon sugar alcohols in infested ash trees, were determined by tandem mass spectrometry and confirmed with standards. CONCLUSIONS: This preliminary study suggests that leaf spray mass spectrometry of ash tree leaflets provides a potential tool for the early detection of ash tree infestation by the emerald ash borer. Copyright © 2016 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".