Effects of Tag Type and Environment on Migratory Success of Sockeye Salmon (Oncorhynchus nerka) Smolts Through a Counting Fence
Bibliographic record
Abstract
Understanding behavior and quantifying survival is vital to the conservation of anadromous fishes. Sockeye Salmon (Oncorhynchus nerka) are one of the most economically and culturally important fish in the world, but some populations are declining, particularly in the Fraser River watershed in British Columbia, Canada. Within the Fraser River basin, Chilko Lake is one of the most prolific Sockeye Salmon populations, and Fisheries and Oceans Canada (DFO) has operated a counting fence at the mouth of the river for several decades to enumerate outmigrating smolts. Previous studies identified the clear water landscapes immediately downstream of the fence as high-risk, with high mortality of migrating smolts relative to larger, more turbid systems further downstream. In this thesis, I used passive integrative transponder (PIT, n = 358 age-1 smolts and n = 251 age-2 smolts) and acoustic telemetry (n = 208 age-2 smolts) to assess behavior and estimate survival of Chilko Lake Sockeye Salmon smolts in the 1.3-km upstream of the counting fence. Mark-recapture Cormack-Jolly-Seber models indicate that this 1.3-km stretch of the migration is high-risk (survival ranging from 37.7% - 62.2%). Survival estimates were similar among age classes and tag types, but age-2 fish may experience slightly higher survival than age-1 fish, with age-1 fish representing ~96% of the outmigrating population. Travel duration from release to the counting fence varied widely (1.3 hours – ~18 days days), suggesting complex behavioral patterns in this specific area. After investigating several covariates, fork length, mass, and a condition metric (residuals of a mass-length relationship) appeared correlated with survival, but the directions of relationships were not consistent across age and tag types. Further investigations of fine-scale behavioral patterns revealed that it is common for smolts to make several attempts to pass the fence, potentially causing delay and a depletion of energy stores. Consistent with previous studies, I presented evidence that increased co-migrant density appears to promote successful escapement at the fence. I identified a new high-mortality landscape and showed that a counting fence used by fisheries managers annually over the past 70 years is likely negatively affecting survival and the timely passage of out-migrating smolts.
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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 | 0.001 |
| 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) | 0.001 | 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".