Genetic diversity and disease resistance: some considerations for research, breeding, and deployment
Bibliographic record
Abstract
Fungal pathogens present a complex challenge for genetic management of forest trees. The need is for disease resistance that withstands mutations and genetic shifts in pathogens. Also desirable are defences against new and dangerous pathogens. An understanding of how hosts and pathogens can continue to coexist should help. Experience from agriculture has allowed modelling of pathosystems, the genetic variations within hosts and pathogens that permit coexistence. While it is impractical to construct a comprehensive model, two phenomena seem generally conducive to stability: a cost of "virulence" in pathogen fitness and a multiplicity of host resistance mechanisms. However, other factors, notably indirect costs of resistance, are very difficult to model. Overall, the diversity of behaviour of models, of the nature of resistance and virulence genes, and of biology of both hosts and pathogens precludes any unique formula for stability. For current crops, genetic diversity offers risk spread for susceptibility to a new and serious pathogen or pathotype. For longer-term breeding, relatively rare resistance may be useful, but pedigreed breeding populations typically entail very finite population sizes. Providing for selecting within improved production populations may therefore be needed. This would give breeders technical challenges, and give forest managers opportunity costs and major logistical challenges.
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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".