A Comparison of Stock and Individual Identification for Chinook Salmon in British Columbia Provided by Microsatellites and Single-Nucleotide Polymorphisms
Bibliographic record
Abstract
Abstract The following questions were addressed in this study: (1) If a suite of 12–15 microsatellites were used in the genetic stock identification (GSI) of Chinook salmon Oncorhynchus tshawytscha, which microsatellites should be in the suite? (2) How many microsatellites are required to provide stock identification resolution equivalent to that of 72 single-nucleotide polymorphisms (SNPs)? (3) How many SNPs are required to replace the current microsatellite baselines used in GSI applications? (4) If additional GSI power is required for microsatellite baselines, what is the incremental increase provided by SNPs and microsatellites? The variation at 29 microsatellite loci and 73 SNP loci was surveyed in 60 populations of Chinook salmon in 16 regions in British Columbia. Microsatellites with more observed alleles provided more accurate estimates of stock composition than those with fewer alleles. The options available for improving the accuracy and precision of stock composition estimates for a 12-locus Fisheries and Oceans Canada (DFO) microsatellite suite range include adding either 4 microsatellites or 25 SNPs to the existing suite to achieve an overall population-specific accuracy of 86% across 60 populations. For the 13-locus Genetic Analysis of Pacific Salmon (GAPS) microsatellites, either 2 microsatellites or 20–25 SNPs can be added to the existing suite to achieve approximately 86% population-specific accuracy in estimated stock composition. The enhanced DFO (16 loci) and GAPS (15 loci) microsatellite baselines were projected to require 179 and 166 SNPs, respectively, for equivalent precision of the population-specific estimates. The level of regional accuracy of individual assignment available from the enhanced DFO and GAPS suites of microsatellites was projected to require 90 and 82 SNPs, respectively. The level of individual assignment to specific populations available from the enhanced DFO and GAPS suites of microsatellites was projected to require 137 and 121 SNPs, respectively. Received January 10, 2011; accepted August 22, 2011
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".