Assessing Reactions of Genetically Improved and Local Cassava Varieties to Cassava Mosaic Disease (CMD) Infection in a Savannah Region of the DR-Congo
Bibliographic record
Abstract
The responses of eight genetically improved and eight local cassava varieties to cassava mosaic disease (CMD) were evaluated under field conditions at two sites, in Eastern Kasa?, region of the DR-Congo). The varieties were planted using randomized complete block design with three replications. The rate of cuttings sprouted varied significantly from variety to variety and from location to location. Local varieties were severely infected than improved varieties throughout the trial period. In general, the level of CMD incidence for genetically improved varieties was below 15% while it reached 100% for the local cassava varieties six months after planting (MAP). This trend was also observed for the CMD severity and gravity. The mean scores for CMD severity were 2 and 3.6 for genetically improved and local varieties, respectively at 6 MAP. CMD gravity for improved varieties was below 21% for genetically improved varieties and exceeded 85% for local varieties at the end of trials. Area Under the Severity index Progress Curve (AUSiPC) and Area Under Disease Progress Curve (AUDPC) estimates confirmed that improved varieties were moderately infested comparatively to local varieties. Molecular analysis is being conducted to determine the genetic variability and complexity of the cassava mosaic virus strains involved.
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 | 0.000 | 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 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".