A seafood risk tool for assessing and mitigating chemical and pathogen hazards in the aquaculture supply chain
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intricate links between aquatic animals and their environment expose them to chemical and pathogenic hazards, which can disrupt seafood supply. Here we outline a risk schema for assessing potential impacts of chemical and microbial hazards on discrete subsectors of aquaculture-and control measures that may protect supply. As national governments develop strategies to achieve volumetric expansion in seafood production from aquaculture to meet increasing demand, we propose an urgent need for simultaneous focus on controlling those hazards that limit its production, harvesting, processing, trade and safe consumption. Policies aligning national and international water quality control measures for minimizing interaction with, and impact of, hazards on seafood supply will be critical as consumers increasingly rely on the aquaculture sector to supply safe, nutritious and healthy diets.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it