Effectiveness of Positive Selection in Managing Seed-Borne Potato Viruses
Bibliographic record
Abstract
Potato productivity in Kenya has remained below the potential due to the use of infected seed tubers especially by seed borne viruses. A study was carried out to asses the effectiveness of Positive Selection (PS) in managing seed borne potato viruses. Sprouted seed potato tubers of nine genotypes; three commercial varieties and six advanced International Potato Center (CIP) clones were screened in the field for two seasons, Field Generation Three (FG3) and Field Generation Four (FG4), respectively. In each season, experimental plots were divided into two sub-plots in which one sub-plot was planted with seed tubers sourced through Random Seed Selection (RSS) and the other with seed tuber sourced through Positive Selection (PS). Positive selection plots had low visual virus incidences, high number of tubers per hill and yield (t/ha) in both FG3 and FG4 growing seasons irrespective of the genotypes. ELISA test also revealed that Potato Virus S (PVS) (78%) was the most predominant virus followed by Potato Virus Y (PVY) (20%), Potato Leaf Roll Virus (PLRV) (16%) and Potato Virus M (PVM) (9%) in RSS plots either with single or multiple infections. Positive Selection plots recorded low virus incidences of PVS (47%), and PVY, PLRV and PVM were absent in the tubers tested with ELISA. This study concludes that Positive Selection is a good management strategy to manage the seed borne potato viruses, and if adopted, small holder farmers can reduce the yield losses arising from use of virus infected seed tubers.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".