Assessement of the Relationship Between Increase in Heigth of Cassava Growth Rate and Agro-Climatic Parameters in Ilorin Area of Kwara State, Nigeria
Bibliographic record
Abstract
Cassava is primarily produced for food in its various forms and Nigeria has been recognized as the largest producer of the crop in the world. Despite the impacts of various weather parameters on crop production, Cassava can still withstand harsh conditions making it a key crop for protecting small holder farming against climate change. This paper therefore examined the relationship between increase in height of Cassava growth rate and agro climatic parameters. The agro climatic indices appraised were Rainfall, Relative humidity, Temperature and wind speed. Interrelationship between these agro climatic variables and increase in the height of growth rate of Cassava was computed using regression analysis. It was discovered that the four agro climatic variables had relationship with one another at either 95% significant level or 99% level. It was also revealed that there is 75% at 95% significant level in the rate of increase in height and yield of Cassava which was accounted for by relative humidity. It was therefore concluded that increase in the height rate and yield of Cassava due to relative humidity was as a result of combined effects of the three other climatic parameters.
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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 | 0.001 |
| 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 | 0.000 | 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".