Estimating seafood harvest requirements to support the traditional food system of First Nations in British Columbia
Bibliographic record
Abstract
Background: Estimating the subsistence harvest in First Nations is important for developing fishery management strategies. It is common to rely on reported catch values when estimating subsistence harvest, but frequent underreporting and discrepancies between the fish that are caught and those that are consumed can lead to incorrect estimates.\nObjectives: In this study, we aimed to 1) determine the quantity of Pacific fish harvest required to maintain the traditional diet of six coastal First Nations communities in British Columbia (Kitsumkalum, Hagwilget, Skidegate, Nuxalk, Namgis, and Tla amin), and 2) identify gaps in data availability and highlight suggestions for improved methodology in future studies.\nDesign: We used food frequency questionnaires from the 2011 First Nations Food, Nutrition & Environment Study to determine food use, and the census data from Statistics Canada to determine the population demographics of these communities. We identified 15 culturally important species, including eulachon (Thaleichthys pacificus), Pacific halibut (Hippoglossus stenolepis), sockeye salmon (Oncorhynchus nerka), and chinook salmon (Oncorhynchus tshawytscha) based on their local consumption prevalence. Employing a proportional projection, we estimated the annual consumption rate for each species by sub-population and used conservative edible yield estimates to determine the total catch needed to sustain traditional seafood consumption levels for average and upper consumption frequencies.\nResults: Harvest requirements varied widely between fish species and the type of projection employed; the species with the highest subsistence harvest was sockeye salmon (Oncorhynchus nerka) at 5822.858 kg/year, equivalent to approximately 1459 3405 fish. For future studies, we suggest working with FNs communities to establish community-specific harvest schedules, and on focusing on harvest-sharing networks and the relationship of FNs living on-reserve and off-reserve to estimate subsistence harvest requirements with more accuracy.\nConclusions: The results of this study establish a baseline of traditional seafood consumption in First Nation communities in BC, which can will be useful for fisheries management planning. Keywords: First Nations; food security; subsistence harvest; consumption survey; Indigenous fisheries; Pacific Maritime ecozone; British Columbia; on-reserve and off-reserve
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".