Bibliographic record
Abstract
Physical mapping is a key methodology for determining the genome structure of Salmonella and revealing genomic differences among different strains, especially regarding phylogenetic relationships and evolution of these bacteria. In fact, physical mapping is the only practical approach to genomic comparisons among Salmonella involving large numbers of strains to document their insertions, deletions, and rearrangements that may be related to pathogenesis and host specificity. The core technique in physical mapping is pulsed field gel electrophoresis (PFGE), which can separate DNA fragments ranging from less than one kilobase to several thousand kilobases. After genomic DNA has been cleaved by an endonuclease and the DNA fragments have been separated on PFGE, a number of techniques will be employed to arrange the separated DNA fragments back to the original order as in the genome. These techniques include Southern hybridization with known DNA as the probe to identify the DNA fragments, Tn10 insertion inactivation to locate genes and identify the fragments that contain these genes, double cleavage to determine the physical distances of cleavage sites between different endonucleases for further refining the physical map, and I-CeuI partial cleavage to lay out the overall genome structure of the bacteria. The combination of these mapping techniques makes it possible to construct a Salmonella genome map of high resolution, sufficient for comparisons among different Salmonella lineages or among strains of the same lineage.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".