Bibliographic record
Abstract
Maize (Zea mays) is always preferred to other crops, and it is fast becoming an industrial crop in Sub-Saharan African countries. Nigeria has been divided into low, medium, medium to high and high maize production potential groups. Traditionally, maize was mostly grown in forest ecology in Nigeria but large scale production has moved to the savanna zone, especially the Northern Guinea savanna, where yield potential is much higher. Maize yields in Nigeria is still very low due to biotic, abiotic agronomic factors like soil infertility, pests and diseases, drought, unavailability of improved germplasms, weeds, unremunerative prices, uncertain access to markets etc. Maize pests and diseases in Nigeria include downey mildew, rust, leaf blight, stalk and ear rots, leaf spots and maize streak virus, Striga attack, stem borers, termites, storage insects, beetle etc. Collaborative research efforts in Nigeria led to development of agronomic package for maize production for different farming systems. There are different readily-available ethnic maize dishes in Nigeria and due to lower cost and high starch contents, maize is commonly used as roughage feed for livestock, and also included in poultry feeds. Importance of maize as an easily harvested crop food with potential to mitigate food insecurity and alleviate poverty cannot be over-emphasized in the developing world. Key words: Agronomy, ethnic foods, food insecurity, fertilizer, maize, sub-sahara Africa.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".