Comparative strain typing of<i>Rhizobium leguminosarum</i>bv.<i>viciae</i>natural populations
Bibliographic record
Abstract
372 natural isolates of Rhizobium leguminosarum bv. viciae, rescued from nodules of pea plants grown in an agricultural field in northern Italy, were analyzed by different methods. Three DNA-based fingerprinting techniques were lined up to compare their relative degree of resolution and possible advantages of each approach. The methods included (i) Eckhardt gel plasmid profiles, (ii) pulsed-field gel electrophoresis (PFGE) of genomic large fragment digests, and (iii) random amplified polymorphic DNA (RAPD) profiles, generated with arbitrary primers. The scheme also involved the isolation of a number of different isolates per nodule to estimate the level of intra-nodular variability. It was therefore possible to evaluate the frequency of double and multiple occupancies, and the proportion of the alternative profiles sharing the same nodule, generally resulting in a numerically dominant, main representative accompanied by a secondary one with a slightly different fingerprint. This finding revealed that the different profiles within a nodule are normally due to bacteria derived from the same single invader following genetic alterations possibly occurred during infection, e.g., by plasmid loss. The analysis of 31 nodules revealed 16 different patterns, representing the most frequently occurring nodulation-proficient isolates of the natural soil examined, five of which were found with frequencies around 15%. The sensitivity of the methods in differentiating isolates was compared. The relatedness of the different natural rhizobial isolates was investigated by densitometrical gel analysis of the fingerprints, allowing a comparison of the results. One of the most interesting conclusions was that the degree of information yielded by the plasmid gel profiling alone, carried out by simple visual inspection without software-aided analyses, was surprisingly high, as it enabled a placement of the isolates, whose accuracy, in terms of relatedness, was subsequently confirmed by each of the two genomic methods.
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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 | 0.001 |
| 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) | 0.000 | 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".