Assigning individual fish to populations using microsatellite DNA markers
Bibliographic record
Abstract
Abstract New statistical developments combined with the use of highly polymorphic microsatellite DNA markers enable the determination of the population of origin of single fish, resulting in numerous new research possibilities and applications in practical management of fish populations. We first describe three main categories of methods available, i.e. (i) assignment tests and related methods, (ii) discriminant function analysis and (iii) artificial neural networks. In all these, individuals can be assigned to the population from which their multilocus genotypes are most likely to be derived. Assignment tests are based on calculations of the likelihood of multilocus genotypes in populations, based on allele frequencies. Discriminant function analysis is based on multivariate statistics, whereas artificial neural networks formulate predictions through exposure to correct solutions. Assignment tests are the methods of choice when considering genetic data alone, whereas discriminant function analysis and artificial neural networks may be useful when genetic data are combined with, for instance, morphological and ecological data. Assignment tests can be used to assess the genetic distinctness of populations, for discriminating among closely related species and to directly identify immigrants or individuals of immigrant ancestry, and thereby study patterns of dispersal among populations, including sex‐biased dispersal. In a conservation context, assignment tests can be used to assess the genetic impact of domesticated fish on wild populations and for determining if extant fish populations are in fact indigenous or descendants from stocked fish or strayers, and they can be applied in forensics, for instance to reveal poaching. Assignment tests are at present most useful for studies of freshwater and anadromous fishes owing to stronger genetic differentiation among populations than in marine fishes. However, some genetically divergent populations of marine fishes have been discovered, which could be used as natural laboratories for studying dispersal and gene flow. It is foreseen that ongoing developments in statistical methods, combined with improved techniques for screening large numbers of loci, will permit assignment methods to become standard tools in studies on the biology of fishes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".