Interrelationship and Path Coefficient Analysis of Some Growth and Yield Characterestics in Sesame (Sesamum Indicum L.)
Bibliographic record
Abstract
Field experiments were conducted during 2005 and 2006 rainy seasons at the Teaching and Research Farm,Faculty of Agriculture, Adamawa State University, Mubi, Nigeria (Latitude 100 15’N and longitude 130 16’ E atan altitude of 696 m above sea level) to study the effect of nitrogen (N) and phosphorous (P) rates on somegrowth and yield characteristics of sesame as well as to determine the interrelationship, path coefficient analysisand percentage contributions of these growth characters to seed yield. The treatments consisted of four N rates: 0,30, 60 and 90 kg ha-1 and four P rates: 0, 15, 30 and 45 kg ha-1. These treatments in factorial combinations werelaid out in split plot design with N rates assigned to main plots and P rates assigned to sub plots and werereplicated four times. The following data were collected; number of branches per plant, leaf area per plant, plantheight and seed yield per hectare and were subjected to correlation and path coefficient analyses. Result obtainedshowed a positive relationship among the characters measured which also contributed meaningfully both directlyand indirectly to total seed yield per plant with number of branches and plant height making the highest directcontributions, respectively. Hence these two may serve as a basis for selection in sesame crop improvement.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".