Striga Infestation in Kenya: Status, Distribution and Management Options
Bibliographic record
Abstract
Striga spp. is considered to be the greatest biological constraint to food production in sub-Saharan Africa, a more serious problem than insects, birds and plant diseases. They are among the most specialized root-parasitic plants inflicting serious injury to their host depriving them water, minerals and photosynthate. The greatest diversity of Striga spp. occurs in grassland. However, Striga hermonthica mainly occurs in farmland infecting grasses. The parasite devastating effect is accomplished prior to its emergence from the soil. It may cause yield losses in cereals ranging from 15% under favourable conditions to 100% where several stress factors are involved, thereby affecting the livelihood of millions of resource-poor farmers. Piecemeal approach to address one aspect of Striga problem at a time has been a setback in technology transfer to producers. Future Striga control programs should not be conducted separately, but should rather be conducted in an integrated approach that combines research talents of various institutions. This will facilitate collaborative research and achieve qualitative interaction between stakeholders, which can easily produce reliable technologies that are practical and available to farmers. Striga being a pervasive pest, time is of essence in controlling it. There is an urgent need for the establishment of policies to promote, implement, and ensure a long-term sustainable Striga control program.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".