An Examination of Indigenous Halibut Fishing Technology on the Northwest Coast of North America
Bibliographic record
Abstract
Abstract As global fish populations face threats from climatic change and human exploitation, the value of Indigenous knowledge and technology for guiding restoration and conservation efforts is gaining increasing recognition. Indigenous fishers on the Northwest Coast of North America traditionally employed sophisticated harvesting practices developed through long-term relationships with marine ecosystems, which promoted sustained harvests. Here we examine traditional Pacific halibut (Hippoglossus stenolepis) hook technology which has been shown to reduce bycatch of nontarget species and is often described as highly size-selective. We investigate this technology using ethnographic information, analysis of fishing equipment curated in museums, and measurements of modern halibut. We identify regional variation and overlap in hook styles, expand previously established hook typologies, and observe the greatest number of hooks and the most stylistic diversity originating from Haida Gwaii, a location where available zooarchaeological data indicates high halibut abundance. We demonstrate that two measurements (hook lip-gap and barb-area size) disproportionately influence the maximum and minimum body size. Based on hook and modern fish measurements, we estimate the sample of hooks targeted fish between 53 and 145 cm in length, indicating a broad but flexible size-selectivity that has presentday relevance for fisheries conservation, including nonmortality slot-limit fishing.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".