Application of the Genetic Mark–Recapture Technique for Run Size Estimation of Yukon River Chinook Salmon
Bibliographic record
Abstract
Abstract We present an application of the genetic mark–recapture technique to estimate salmon run size in a large river. Application of this technique requires modifications to estimation methodology. Under a typical Lincoln–Petersen mark–recapture estimation of salmon run size (N = M/p), individual fish are captured and marked (M) in the lower river and are recaptured (m) at escapement (E: the number of fish reached spawning ground) monitoring sites selected upriver where the proportion of marked individuals (p = m/E) is estimated. In this genetic mark–recapture technique, the marked individuals are not captured and recaptured, but rather the naturally distinctive genetic (marked) population is captured and recaptured. Genetically, the lower river population is a mixture of multiple genetic stocks, whereas the upriver escapement population consists of a single genetic stock. Hence, the mark–recapture experiment (N = M/pm) is reversed. The proportion of “marked” genetic stock (pm) is estimated in the lower river, and size of the “marked” stock in the lower river (M) is estimated by summing its upriver escapement (Em) and harvest (Cm) between the lower and upper portions of river (M = Em+Cm). The harvest is calculated as a product of total upriver harvest (C) and the proportion of the “marked” stock (pcm) in the harvest (Cm = C·pcm). Further, when the proportion of multiple genetic stocks (pk) is identified, stock-specific run size (Nk = N·pk), escapement (Ek = Nk−Ck, where Ck = C ·pck), and exploitation rate (Exk = Ck /Nk) can also be estimated, which provides substantially more information than does the conventional approach. We illustrate an application of this technique for estimating run size of Chinook Salmon Oncorhynchus tshawytscha in the Yukon River, Alaska. Received June 6, 2013; accepted November 20, 2013
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".