Assessment of zinc-solubilizing bacterial isolates for plant growth promoting traits and water stress tolerance behaviour
Bibliographic record
Abstract
The present study elucidates the efficiency of zinc solubilization and the plant growth-promoting (PGP) traits exhibited by selected zinc-solubilizing bacterial (ZSB) strains. Qualitative analysis unveils distinct variations in solubilization efficiency, with isolate FMBR110 demonstrating exceptional proficiency in solubilizing zinc from various insoluble sources. PGP traits, encompassing phosphate solubilization, siderophore production, indole-3-acetic acid (IAA) synthesis, exopolysaccharide (EPS) generation, hydrogen cyanide (HCN) emission, and ammonia production are quantified, spotlighting the functional diversity among isolates. Notably, FMAR106 showed the highest phosphate solubilization zone value (2.20 cm), while FMBR110 exceled in siderophore production (4.37 cm halo zone), IAA synthesis (24.09 µg/ml), and stands out as the sole producer of HCN. Furthermore, isolate FMBR110, exhibited remarkable tolerance to water stress, even in high water saturated soil conditions. The study accentuates the importance of selecting ZSB strains tailored to specific water conditions in agriculture, offering promising solutions for sustainable crop production, particularly in high water-stressed regions or flooding conditions.
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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) | 0.001 | 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".