Genetic Diversity of Rhizobia Nodulating Alfalfa in Iraq as a Source of More Efficient Drought Tolerance Strains
Bibliographic record
Abstract
Sinorhizobium meliloti is a gram-negative, soil bacteria, which gain a huge importance deserved to their capability in fixing nitrogen symbiotically with an important fodder crop legume-alfalfa (Medicago sativa). This study aims to (i): isolate indigenous Sinorhizobium meliloti from different field sites in Iraq; (ii): assess the isolates tolerance to induced water shortage using polyethylene glycol-6000; (iii): evaluate genetic diversity and genetic relationships among isolates of natural population with drought tolerant abilities. Drought tolerance analysis disclose great variations between Sinorhizobium isolates, the highest tolerant isolates to drought were 12 from whole thirty (40%), endured from -3 up to -4 MPa(Mega pascal), at the same time the drought sensitive isolates tolerated up to -1.5 MPa, but for isolate Bs58 which tolerated up to -1 MPa. The growth decrease with the increase of drought stress. REP-PCR method was an excellent technique to distinguish between the S. isolates based on their tolerance to different water potential levels, the REP-PCR gave a significant band about 700bp (base pair) in size, which were present in all drought moderate and tolerant isolates, while this band was absent in all drought sensitive isolates(except one isolate). Also the ERIC-PCR method cleared the absence of two significant bands 300bp and 600bp from all drought sensitive isolates. UPGMA analysis based on rep-PCR revealed two groups branching at a similarity of 35%, first group included only one isolate Bs16, which was a drought moderate tolerant; second group contained the rest of isolates, yet the latter splits into two subgroups with 68% similarity; the first subgroup comprised all sensitive isolates with 80% similarity among them, the second subgroup included the tolerant and moderate isolates with similarity 88%.
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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 | 0.001 | 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) | 0.001 | 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".