Varietal Productivity and Planting date effect on the Growth and Yield of Cucumber ( Cucumis sativus L.) in Owo, South Western Nigeria
Bibliographic record
Abstract
A field experiment was conducted during the wet season of 2014 at the Teaching and Research Farm of the Rufus Giwa Polytechnic, Owo to evaluate the performance of four varieties of cucumber (Ashley, Nonadini, Murano and Ande) at different planting dates in order to determine the appropriate date during the wet season to achieve optimum yield. The experiment was a 4 x 4factorial laid out in a Randomized Completely Block Design (RCBD). Planting was done on the 19 th and 26 th of April and 3 rd and 10 th of May, 2014. The results of the study indicates significant differences (P< 0.05) among the varieties in terms of vine length, number of branches, leaf area, number of fruit per plant and total fruit weight per hectare. The highest fruit yield per hectare was obtained in the April 26 th and May 3 rd planting dates. Nonadini and Ashley varieties consistently had significant higher yields than the other two varieties. There was interaction between varieties of cucumber and planting dates. The highest yields were obtained from Nonadini and Ashley varieties during April 26 th and May 10 th planting dates.
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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 | 0.000 | 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".