on analysis of inter-simple sequence repeat markers
Bibliographic record
Abstract
In order to investigate the genetic diversity of Ligula intestinalis populations, nine inter-simple sequence repeat (ISSR) markers were applied to populations from nine geographical areas around the world and 10 host species. The 110 loci selected from the ISSR patterns produced revealed high variability among the analysed samples, with a polymorphism of 100 % and a global coefficient of gene differentiation estimated by Neis index (GST) of 0.776. Major genetic differentiation was found to be correlated to five broad geographical regions (Europe, China, Canada, Australia and Algeria). Nevertheless, no significant genetic variation was found among European isolates, although they originated from disparate geographical localities and⁄or unrelated hosts. Classical classification methods: maximum parsimony and factorial correspondence analysis were compared with an advanced statistical method: the self-organizing map (SOM). The results demonstrated that the ISSR approach is rapid and inexpensive and provides reliable markers to assess genetic diversity of L. intestinalis. Furthermore, SOM artificial neuronal networks are considered to provide an efficient alternative tool for mapping the genetic structures of parasite populations. Key words: inter-simple sequence repeat – self-organizing map – genetic diversity – parasite clustering – Ligula intestinalis – geographic isolation
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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 | 0.001 |
| 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".