Correlation Studies in Early Clonal Generation under Water Logging Condition in Sugarcane for Yield and Its Attributing Traits
Bibliographic record
Abstract
An experiment was carried out during 2021-22 at Sugarcane Research Institute, Dr. RPCAU, Bihar using 24 clones that were planted in augmented design along with two checks. Data was recorded for the nine characters. There was a positively significant correlation found among cane yield and the following characteristics: cane diameter at harvest, number of millable canes at harvest, germination percentage at 45 days after planting, and number of shoots at 120 days after planting. Whereas, the number of aerial roots per node showed a negative correlation with cane yield. Traits viz., germination % at 45 days after planting, number of shoots at 120 days after planting, cane diameter at harvest, single cane weight, number of millable canes at harvest, HR Brix in November had direct and positive effect on cane yield, among these, number of millable canes at harvest showed highest direct and positive effect followed by single cane weight on cane yield, whereas, plant height at harvest, number of aerial roots per node, HR Brix in December and January showed negative direct effect on cane yield. The characters that showed significant correlation and positive direct effect can be selected further to obtain higher cane yields in sugarcane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".