Analysis of Correlation and Coefficient for Seven Flax Varieties by Two Types of Sprinkler Heads System
Bibliographic record
Abstract
Abstract The experiment was carried out at Daquq Research Station, Kirkuk Governorate during 2018-2019 to evaluate solid set sprinkler irrigation indicators in the cultivation of flax varieties. The studied flax traits included plant height, number of secondary branches per plant, number of capsules per plant, number of seeds per capsule, weight of 1000 seeds, seed yield per plant, biological yield per plant, harvest index, and seed yield per hectare. The results showed that the highest genetic correlation was between stem diameter and seed yield, reaching 2.89 under the Canadian sprinkler head. The highest environmental correlation was observed between the number of main branches and seed yield, reaching 3.50 under the Turkish sprinkler head. The highest phenotypic correlation was between oil percentage and seed yield, reaching 2.38 for the Canadian sprinkler head. Regarding the path coefficient analysis, the Canadian sprinkler head showed the highest direct effect on the harvest index, reaching 0.7136. It also had the highest total effect through the oil-to-seed ratio, which reached 2.7173. For the trait of number of days to 50% flowering, the direct effect was 0.9996. In the interaction experiment between study factors, the highest direct effect was found through the oil-to-seed ratio, reaching 0.8580, with the highest total effect on the oil-to-seed ratio summing up to 1.2849.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".