Ancient fish weir technology for modern stewardship: lessons from community-based salmon monitoring
Bibliographic record
Abstract
ABSTRACT Introduction: The UN Declaration on the Rights of Indigenous Peoples states that indigenous people have a fundamental right to contribute to the management of the resources that support their livelihoods. Salmon are vital to the economy and culture of First Nations in coastal British Columbia, Canada. In this region, traditional systems of management including weirs – fences built across rivers to selectively harvest salmon – supported sustainable fisheries for millennia. In the late-19th century traditional fishing practices were banned as colonial governments consolidated control over salmon. Outcomes: In collaboration with the Heiltsuk First Nation we revived the practice of weir building in the Koeye River. Over the first four years of the project we tagged 1,226 sockeye, and counted 8,036 fish during fall stream walks. We used a mark-recapture model which accounted for both pre-spawn mortality due to variation in temperature, and tag loss, to produce the first mark-resight estimates of sockeye abundance in the watershed (4,600 – 15,000 escapement). Discussion: High river temperatures are associated with increased en route morality in migrating adult sockeye. We estimated pre-spawn mortality ranged from 8 – 72% across the four years of study, highlighting the degree to which climate conditions may dictate future viability in sockeye salmon populations. These results demonstrate the power of fusing traditional knowledge and management systems with contemporary scientific approaches in developing local monitoring.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".