Comparative Evaluation of Colemanite and Sodium Pentaborate as Boron Sources for Rice Grown in Flooded Calcareous Soil
Bibliographic record
Abstract
Boron insufficiency in the agricultural soils is common and wide spread problem in many regions of the world. The effectiveness of crushed ore colemanite as B sources for rice crop under flooded calcareous soil were evaluated in a glass house study. We studied the effects of powder colemanite (PC) and granular colemanite (GC) in comparison with the refined sodium pentaborate fertilizer at the rates of 0, 1, 2, and 3 kg B ha-1 on growth and yield parameters of rice crop as well as the true control (0 kg B ha-1). Sodium pentaborate (SP) and (PC) application of 2 and 3 kg B ha-1 significantly increased the plant height, number of tillers and panicles per plant, number of grains per panicle, weight of 1000 grains and B concentration in grain compared the 0 and 1 kg B ha-1. Rice crop with SP and PC applied at 3 kg B ha-1 produced significantly (18% over the control) higher grain yield than the 0 kg B ha-1 treatment. Pots fertilized with SP and PC produced similar results as grain yield difference between them was not significant so these B fertilizers were very effective in supplying B to rice crop, but GC applied pots produced significantly low yield because of its bigger particle size, due to which B was not released from fertilizer. This study proved that colemanite with smaller particle size is an effective B source and it is cheaper than refined products so it should be applied for harvesting higher yields.
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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".