Issues in Evaluating Fish Consumption Rates for Native American Tribes
Bibliographic record
Abstract
The environmental health goals of many Native American tribes are to restore natural resources and ensure that they are safe to harvest and consume in traditional subsistence quantities. Therefore, it is important to tribes to accurately estimate risks incurred through the consumption of subsistence foods. This article explores problems in conventional fish consumption survey methods used in widely cited tribal fish consumption reports. The problems arise because of the following: (1) widely cited reports do not clearly state what they intend to do with the data supporting these reports, (2) data collection methods are incongruent with community norms and protocols, (3) data analysis methods omit or obscure the highest consumer subset of the population, (4) lack of understanding or recognition of tribal health co-risk factors, and (5) restrictive policies that do not allow inclusion of tribal values within state or federal actions. In particular, the data collection and analysis methods in current tribal fish consumption surveys result in the misunderstanding that tribal members are satisfied with eating lower contemporary amounts of fish and shellfish, rather than the subsistence amounts that their cultural heritage and aboriginal rights indicate. A community-based interview method developed in collaboration with and used by the Swinomish Tribe is suggested as a way to gather more accurate information on contemporary consumption rates. For traditional subsistence rates, a multidisciplinary reconstruction method is recommended.
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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 | 0.091 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".