Session 3: International scab nursery consortium STRATEGIES AND CONSIDERATIONS FOR
Bibliographic record
Abstract
Screening of cereals for reaction to Fusarium head blight (FHB) occurs world-wide and employs diverse methods, or, at best, variations on a basic method. Is there one protocol that is suitable/appropriate for all environments? Or is it best to create an epidemic in any way that can be certain of success, but attempt a uniform analysis to compare reactions of genotypes within and across specific environments? The FHB index commonly used and developed by Charles Snijders of The Netherlands in the 1990s has served us well, but it incorporates only visual symptoms of the disease, i.e. incidence and severity. In societies becoming increasingly conscious of food safety and security, should we consider including additional factors such as Fusarium-damaged kernels (FDK) and deoxynivalenol (DON) as part of the determination of a genotype’s reaction to FHB? For a screening nursery to work well there must be a knowledge base of both the pathogen and the host within a specific environment, in order to manipulate factors to create optimal conditions for disease to occur. Some factors to discuss include inoculum, inoculation method (what types of resistance are we screening for?), timing and number of inoculations, application of misting/irrigation, rating (field/lab – single/multiple), and incorporation of FDK and DON into the analysis. One method of analysis (ISK, Kolb, Illinois) proposes that a proportion of the incidence (I), severity (S), Fusarium-damaged kernels (FDK or K) be added to give a ranking of genotypes. To this we should also consider including DON evaluations. Alternatively, we have been experimenting in Canada with ‘GGEbiplot ’ (Genotype – Genotype X Environment), a software package developed at the
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.154 | 0.042 |
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".