Path Analysis of Yield Per Plant and Its Components of Durum Wheat with Different Sources
Bibliographic record
Abstract
We researched the changes of components of yield per plant and correlation and path analysis of yield per plant and its components of durum wheat.The test materials are 12 varieties(lines) of durum wheat introduced from Italy,Canada and Mexico.The results showed that the yield of lines from Mexico was higher than lines from Italy very significantly.There was no obvious difference in yield per plant among varieties from Canada and lines from Mexico and Italy.There was no obvious difference on the number of effective spikes of durum with different sources.The grain numbers per spike of lines from Mexico was higher than lines from Italy and Canada very significantly,but the 1000 grain weight of lines from Mexico was lower than lines from Italy and Canada very significantly.In our research,the number of effective spikes had positive and negative correlation with grain numbers per spike and 1000 grains weight,and it had significant or very significant positive correlation with the yield per plant.Grain numbers per spike had significant or very significant negative correlation with 1000 grains weight,and it had positive correlation with the yield per plant.1000 grains weight had positive correlation with the yield per plant.The path analysis showed that the number of effective spikes,the number of grains per spike and 1000 grains weight of all varieties had positive effect to the yield per plant.The indirect path coefficient were positive or negative between components of yield and the yield per plant,that means between the yield factors has a inter-promotion and inter-restrictions relations.The number of effective spikes had more contribution to the yield per plant of durum wheat with different sources,which means the number of effective spikes was the base to get high yield.Components of yield of wheat from Mexico were harmonious,so we thought those lines came from Mexico had higher potential to increase yield,the introducing would be successful.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".