Early effects of COVID‐19 on US fisheries and seafood consumption
Bibliographic record
Abstract
The US seafood sector is susceptible to shocks, both because of the seasonal nature of many of its domestic fisheries and its global position as a top importer and exporter of seafood. However, many data sets that could inform science and policy during an emerging event do not exist or are only released months or years later. Here, we synthesize multiple data sources from across the seafood supply chain, including unconventional real-time data sets, to show the relative initial responses and indicators of recovery during the COVID-19 pandemic. We synthesized news articles from January to September 2020 that reported effects of COVID-19 on the US seafood sector, including processor closures, shortened fishing seasons and loss of revenue. Concerning production and distribution, we assessed past and present landings and trade data and found substantial declines in fresh seafood catches (-40%), imports (-37%) and exports (-43%) relative to the previous year, while frozen seafood products were generally less affected. Google search trends and seafood market foot traffic data suggest consumer demand for seafood from restaurants dropped by upwards of 70% during lockdowns, with recovery varying by state. However, these declines were partially offset by an increase (270%) in delivery and takeout service searches. Our synthesis of open-access data sets and media reports shows widespread, but heterogeneous, ramifications of COVID-19 across the seafood sector, implying that policymakers should focus support on states and sub-sectors most affected by the pandemic: fishery-dependent communities, processors, and fisheries and aquaculture that focus on fresh products.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".