Population Structure and Run Timing of Sockeye Salmon in the Skeena River, British Columbia
Bibliographic record
Abstract
Abstract Determination of run timing is an important component of salmonid fisheries management and was the major focus of the current study. Population structure of Sockeye Salmon Oncorhynchus nerka was examined in the Skeena River, northern British Columbia. Variation at 14 microsatellites was surveyed for 27 populations in the drainage. There were 9,473 individuals sampled in a lower river test fishery during 2000–2011 in order to provide information on relative abundance and time of arrival of specific populations or stocks near the mouth of the river. Within-lake or within-river tributary structuring of populations was the general pattern observed, with 10 populations from Babine Lake clustering together in 91% of dendrograms evaluated, and two populations from Lakelse Lake clustering together in 100% of dendrograms evaluated. The 27 populations sampled were arranged in 12 reporting groups for genetic stock identification applications. The estimated stock composition of known-origin mixtures was within 2% of the correct estimate for all 12 reporting groups present in the mixtures. Sockeye Salmon typically began arriving at the test fishery on the lower Skeena River by June 10, peaking in daily abundance in late July or early August, and finished migrating past the test fishery by mid-September. Relative timing of the 12 reporting groups, from earliest to latest, was as follows: Lakelse Lake, Alastair Lake, Zymoetz River, Morice Lake, Kispiox River, Sustut Lake, Babine Lake, Slamgeesh Lake, Motase Lake, Bear Lake, Kitsumkalum Lake, and Kitwanga Lake. Genetic mixed-stock analysis, coupled with a test fishery in the lower river, can assist managers in regulating fisheries directed at Skeena River Sockeye Salmon. Received February 12, 2013; accepted December 4, 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".