Population Structure and Run Timing of Steelhead in the Skeena River, British Columbia
Bibliographic record
Abstract
Abstract Identification of population-specific run timing is an important component of salmonid fisheries management and was a major focus of our study. Population structure of steelhead Oncorhynchus mykiss was examined in the Skeena River of northern British Columbia. Variation at 14 microsatellites was surveyed in 3,062 steelhead sampled from 17 populations in the drainage. During 1998–2010, 6,691 individuals were sampled in a lower river test fishery to obtain information on relative abundance and time of arrival of specific populations near the river mouth. The genetic differentiation index F ST calculated over all populations and loci was 0.021; individual locus values ranged from 0.017 to 0.045. Differentiation in steelhead allele frequencies among populations was approximately 14 times the differentiation observed among years within populations. A regional structuring of populations was the general pattern observed, with steelhead populations from the upper portion of the drainage clustering together in 87% of dendrograms evaluated and those in the lower portion of the drainage clustering together in 100% of dendrograms. The 17 populations sampled were arranged in nine reporting groups for genetic stock identification applications. The estimated stock composition of a simulated known-origin mixture was within 2% of the correct estimate for seven of the nine reporting groups present in the mixture. The stock composition of an actual known-origin sample was estimated within 2% of the correct estimate for eight of the nine reporting groups present. Application to actual samples from the test fishery indicated that upper drainage populations generally migrated through the lower river earlier than other populations, whereas lower river populations typically migrated later. Genetic mixed-stock analysis can assist managers in regulating fisheries to maintain productivity of Skeena River steelhead. Received July 25, 2011; accepted December 15, 2011
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".