Discrimination between Eggs from Stink Bugs Species in Europe Using MALDI-TOF MS
Bibliographic record
Abstract
In the current paper, we used a method based on stink bug egg-protein immobilization on filter paper by drying, followed by post-(storage and shipping) extraction in acidified acetonitrile containing matrix, to discriminate between nine different species using MALDI-TOF MS. We obtained 87 correct species-identifications in 87 blind tests using this method. With further processing of the unblinded data, the highest average Bruker score for each tested species was that of the cognate reference species, and the observed differences in average Bruker scores were generally large and the errors small except for Capocoris fuscispinus, Dolycoris baccarum, and Graphosoma italicum, where the average scores were lower and the errors higher relative to the remaining comparisons. While we observed clear discrimination between the nine species using this method, Halyomorpha halys and Piezodorus lituratus were more spectrally related than the other pairwise comparisons.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".