Isolation and Identification of Plant Growth Promoting and Chromium Uptake Enhancing Bacteria from Soil Contaminated by Leather Tanning Industrial Waste
Bibliographic record
Abstract
Hexavalent chromium is considered as a priority pollutant. Phytoremediation has been widely pursued for the cleanup of heavy metal from contaminated area. The success of phytoremediation is depending on two factors: metal accumulating capability and biomass production of the plants. This paper reports on the isolation and characterization of rhizobacteria having ability to promote plant growth and increase its chromium uptake. Thirty nine of bacterial isolates were obtained from the rhizosphere of wild plants (Sida sp., Sida acuta, Sida rhombifolia, Eupatorium sp., Acelypha sp, Acelypha indica, Amaranthus caudatus, Borreria sp., Leucas lavandulifolia, Eleusine indica, Pennisetum purpurium, Imperata cylindrical, and Vigna sinensis) grow well on soil contaminated by leather tanning industrial waste. Three isolates, namely I26, I30, and I37, have an ability to enhance biomass production of maize (Zea mays) by 2.3, 2.6, and 4.0 times higher compare to the uninoculated one, respectively. The isolates also have an ability to increase chromium uptake by the maize from 7 to 14times. All of the isolates increase the accumulation of Cr in the maize root.The 16S rDNA gene sequence of the isolates relates them to Agrobacterium tumefaciens.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".