Diagnostic fragmentation filtering for the global LC-HRMS/MS analysis of ergot alkaloids and indole–diterpenoids in Norwegian and Canadian Claviceps species
Bibliographic record
Abstract
The understanding of the cereal disease caused by ergot (C. purpurea) is important with reference to wildlife and agriculture as ergot produces toxins. The sample set used in this study consisted of 66 Claviceps sclerotia from five distinct geographical locations: Eidsvoll, Larvik, Maridalen, Telemark (all in Norway) and Saskatchewan in Canada. Genotyping of the sclerotia revealed that the samples comprised of three Claviceps species: G1 (C. purpurea sensu lato), G2 (C. humidiphila) and G2a (C. arundinis). For screening and recognition of already classified and potentially novel toxins, diagnostic fragmentation filtering was applied for the extraction of the entire ergot alkaloid and indole-diterpenoid metabolome from the high-resolution mass spectrometry data. Principal component analysis and partial least squares revealed species-specific alkaloid patterns. G2a was found to be host specific.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".