Application of a method for estimating effective population size and admixture using diagnostic single nucleotide polymorphisms (SNPs): implications for conservation of threatened Paiute cutthroat trout (<i>Oncorhynchus clarkii seleniris</i>) in Silver King Creek, California
Bibliographic record
Abstract
The threatened Paiute cutthroat trout ( Oncorhynchus clarkii seleniris , PCT) is endemic to Silver King Creek, California, USA, which was stocked with non-native trout beginning in 1930. Single nucleotide polymorphism (SNP) and microsatellite data reveal that the trout population in Silver King Creek is weakly structured and composed of introgressed California golden trout ( Oncorhynchus mykiss aguabonita , CAGT), hatchery rainbow trout ( Oncorhynchus mykiss , RT), and some native PCT. Two SNP groups were analyzed: (i) one mitochondrial and five autosomal SNPs, diagnostic between Lahontan cutthroat trout ( Oncorhynchus clarkii henshawi ) or PCT and CAGT or RT and (ii) one mitochondrial and five autosomal SNPs nearly diagnostic between CAGT and RT. The five autosomal cutthroat–rainbow SNPs were used to jointly estimate the cutthroat trout mixing proportion in Silver King Creek and effective population size (Ne) of the admixed population, using a coalescent-based maximum likelihood method. Given the stocking history of Silver King Creek, there are two different scenarios that bound the range of expected point estimates for Ne. We obtain point estimates of Ne = 150 and Ne = 750 for Silver King Creek under these two scenarios. This method will be useful in cases with differentiated taxa and in prioritizing conservation and restoration programs where the populations of concern are introgressed.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".