Can traditional methods of selecting food accurately assess fish health?
Bibliographic record
Abstract
Indigenous peoples living in Canada’s north have long-valued the livers of Burbot (Lota lota) as a traditional food source; however, there has been concern relating to liver quality and potential contaminants. In this study, livers of Burbot collected in lower Mackenzie River were ranked using a traditional appearance-based assessment. These rankings were compared to a variety of biological and contaminant metrics. Livers ranked “most palatable” had a significantly higher mass and lipid content and were from younger fish with greater hepatosomatic index and total mass and had lower parasite intensities. There were no differences in the concentrations of persistent organic pollutants or metals, except copper, which although still well below consumption guidelines, was significantly higher in fish with livers that appeared most palatable. The results of this study demonstrated that traditional methods effectively assessed the quality of livers by selecting for the most nutritious (high lipid levels) and safest (low parasite loading) food. This method could be incorporated into a community-based monitoring framework as a rough index of overall fish and ecosystem health; however, would not be effective in screening food for anthropogenic contaminants. This study highlights the importance and value of linking traditional knowledge into scientific studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".