Growth Response and Sugar Accumulation in First Ratoon Sweet Sorghum: Effects of Biochar and Shoot Number Manipulation
Bibliographic record
Abstract
Sweet sorghum stems contain sap rich in lignocellulose and saccharides, making the plant a valuable source of high-quality forage, ethanol, and food products.This study aimed to investigate the effects of biochar application and shoot number manipulation on the growth response and sugar content of stem sap in first ratoon sweet sorghum.A Completely Randomized Block Design (CRBD) was employed, and data were subjected to an analysis of variance (ANOVA) at a 95% confidence level.In cases of significant differences, Duncan's Multiple Range Test (DMRT) was conducted for post-hoc comparisons.Results demonstrated a significant interaction between biochar application and shoot number manipulation on sugar content in the stem sap.Biochar application had a non-significant effect on the number of leaves and leaf area index, while shoot number manipulation exhibited a non-significant influence on stem diameter and seed weight per plant.These findings contribute to the understanding of optimizing growth and sugar accumulation in first ratoon sweet sorghum, potentially enhancing its applications in forage, ethanol, and food industries.
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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".