Ameliorative Effect of Bacillus sp. MN-54 and Organic Amendments Combination on Maize Plants Growth and Physiology Under Chromium Toxicity
Bibliographic record
Abstract
Inoculation of bacteria increase the growth of maize by facilitating the nutrients uptake and improving the immunity of plants and ability against pathogens, harmful substances such as heavy metals and other diseases. Likewise, organic amendments also increase the growth of plants by providing ample amount of readily available nutrients and improving soil physical conditions and ameliorating heavy metals toxicity. In this study, a pot experiment was conducted to evaluate the interactive effect of bacteria and organic amendments on Cr uptake and growth of maize irrigated with tannery effluent. Biochar and processed animal manure were applied at the rate of 1% (w/w). The experiment was conducted as Completely Randomized Design (CRD) with three replications. During the experiment various growth, physiological and biochemical parameters were measured by using standard procedures. Combination of Bacillus sp. MN-54 and low pH animal manure increased the plant height by 72%, chlorophyll content by 56%, water use efficiency by 68%, electrolyte leakage decreased by 51%, transpiration rate increased by 45%, RWC 57%, stomatal conductance 62%, photosynthetic rate 57% whereas Cr decreased the shoot growth by 45% over their control treatment. Overall the bacterial species evaluated in this study along with biochar and animal manure imparted heavy metal tolerance to maize plants evidenced by improved growth and physiology.
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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) | 0.001 | 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".