Impact of pruning of Diospyros melanoxylon Roxb. (Tendu) bushes on yield and quality of leaves in Maharashtra
Bibliographic record
Abstract
Studies were conducted in SFD controlled pruned tendu bushes dominating forests and CFR controlled non-pruned poles bearing forests in Gondia and Gadchiroli forest divisions of Maharashtra to assess the impact of pruning of tendu bushes on the yield and quality of its leaves. This tree species has attained great economic importance due to its leaves being used as wrappers in the bidi (Indian cigarette) industry and is the good source ofrevenue generation to the states like M.P., Maharashtra, Odisha and U.P. Pruned bushes contributed more than five times healthy leaves (60.33%) than non-pruned poles (11.90%). Gall infested, diseased and defoliated leaves in pruned bushes were recorded less (7.98%, 2.47% and 29.21% respectively), when compared with non-pruned poles (15.37%, 4.97% and 67.76% respectively). The Specific Leaf Area (SLA), which shows the quality of leaves, was found higher (7.46 mm2/mg) in pruned bushes than non-pruned poles (6.39 mm2/mg) exhibiting the better quality of leaves collected from the pruned bushes. Leaf gall in tendu was caused by insect Trioza obsolete, leaf blight disease by Pestalotia diospyri and defoliation by Hypocala rostrata insect. Healthy tendu leaves exhibited maximum carbohydrates and phenols content, while maximum proline was found in diseased leaves and maximum ascorbic acid in insect attacked leaves.
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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".