New data on sterlet (Acipenser ruthenus L.) genetic diversity in the middle and Lower Danube sections, based on mitochondrial DNA analyses
Bibliographic record
Abstract
Poor regulated fishery, pollution, fragmentation and loss of habitat are most important factors influencing decline of sterlet population worldwide. In Middle and Lower Danube region, this species still have significant economic importance since wilde populations are commercially exploited, while Upper Danube populations are dependent on stocking efforts in order to maintain their presence in open waters. Aim of present study is to analyze genetic diversity of sterlet populations from the Middle and Lower Danube and Lower Tisza rivers, as a prerequisite for their effective conservation and management. Analysis of a highly variable D-loop fragment of mitochondrial DNA detected five new haplotypes, while the eight previously identified haplotypes had extended their previous range. Genetic variability could be attributed almost entirely to individuals, with observed lack of population structure. Negative values of neutrality test indicate recent expansion on some sampling locations. Adittionaly, gene flow analysis between Lower and Middle Danube region showed intensive exchange of speciemens. At the same time analysis showed some influence of Tisza dam on gene flow between samples from Tisza and Middle Danube section.Our study indicated the need for a careful planning of sterlet stocking programmes and inclusion of demographic data or catch time-series.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".