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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".