SP17 SNP Data Identify Population of Origin of Pacific Salmon During Oceanic Migrations
Bibliographic record
Abstract
Geneticists use approximately 48 single-nucleotide polymorphisms (SNPs) in each of three species of Pacific salmon to define population structure for the study of migration and harvest management. Pacific salmon are anadromous: populations reproduce in fresh water, undergo vast oceanic migrations to feed and mature, and home to their natal stream to spawn and die. Discrete populations, distinguishable by differences in allelic frequencies, inhabit coastal drainages in Asia and North America north of 40° N Latitude and support important commercial, sport fishing, and subsistence economies. Both harvest and research issues are complex. Some are governed by two international conventions that are keenly interested in population (or country) of origin of migrating salmon: North Pacific Anadromous Fish Commission (http://www.npafc.org/new/index.html, Canada, Japan, Korea, Russia, United States) and the Pacific Salmon Commission (http://www.psc.org, Canada and United States). Why use SNPs for salmon research? Because of the factors above, it is critical that DNA datasets be easily transportable among laboratories and among countries, making SNP data ideal. Also, the migratory and management studies that we describe in this presentation require acquisition of data from large numbers of individuals in a relatively short time, often from unquantitated DNA of variable quality. Although medium multiplex platforms may offer ideal throughput solutions as more salmon SNPs become available, we show that issues of DNA quality, conversion rate, and call rate limit us to singleplex reactions at this time. Even with this limitation, using standard laboratory automation we are able to process 42,000 assays per day to identify population of origin of migrating salmonids.
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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 | 0.000 | 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".