Identification of resistance for <i>Phyllachora maydis</i> of maize in exotic‐derived germplasm
Bibliographic record
Abstract
Abstract Tar spot of maize (Zea mays L.), caused by the obligate biotroph Phyllachora maydis Maubl., is an emerging disease in the United States and Canada, and the identification of sources of resistance for tar spot will enable the development of resistant hybrids. In 2019 and 2020, 25 accessions from the germplasm enhancement of maize (GEM) project containing exotic introgressions in elite backgrounds were evaluated in nine environments for tar spot severity. Environmental conditions had a major influence on disease development, as tar spot severity varied across locations with only four of the nine locations showing moderate to high levels of disease. In five environments, disease levels were low and disease severity data was not collected or used. Accessions were visually evaluated for tar spot during reproductive growth stages in three environments and during vegetative growth stages in one environment. There was a strong correlation between resistance to P. maydis across locations where accessions were evaluated in reproductive growth stages. Two accessions, GEMS‐0066 and GEMS‐0226, were the most resistant and could prove useful for tar spot resistance breeding. These accessions are publicly available and able to be directly used in breeding programs.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".