Preservation of genetic diversity in restocking of the sea cucumber<i>Holothuria scabra</i>investigated by allozyme electrophoresis
Bibliographic record
Abstract
Population genetics analyses should be considered when releasing hatchery-produced juveniles of the sea cucumber Holothuria scabra when spawners from nonlocal populations are used. In New Caledonia, within-region genetic heterogeneity of H. scabra populations (examined through allozyme electrophoresis of 258 animals) indicated high gene flow between nine sites and F ST values did not deviate significantly from zero. However, exact tests indicated that populations at two sites with limited water exchange in the southern location were significantly different from populations at three other locations on the west coast. Inclusion of H. scabra sampled in Bali (n = 90) and Knocker Bay, Australia (n = 47), and comparisons with existing data from the west Pacific (Torres Strait, Solomon Islands, Upstart Bay, Hervey Bay) showed that populations were significantly different (using exact tests) and samples partitioned distinctly using unweighted pair group method with arithmetic mean clustering. Rogers' genetic distance values between populations were significantly related to geographic distances, showing a pattern of isolation by distance. The rapid increase in genetic distance over the first few hundred kilometres supports the view that the spatial extent of any translocation needs to be carefully considered on the basis of knowledge of variation in allele frequencies within the target area.
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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".