Signature of climate-induced changes in seafood species served in restaurants
Bibliographic record
Abstract
Abstract Climate change is causing shifts in biogeography of marine species, towards higher latitude, deeper waters, or following local temperature gradients. Such species distribution changes are affecting global fisheries through increasing the dominance of warmer-water preferred species as ocean temperature increases. Previous modeling analyses projected that climate-induced changes in seafood availability would affect the entire seafood chain. However, observed climate impacts on seafood retailers and consumers have rarely been demonstrated. Seafood restaurants usually rely on the supply of locally caught species, and thus the impacts of changing catches on the food they serve, and consequently on their diners, may be reflected in their menus. In this study, 362 restaurant menus from Vancouver, British Columbia, Canada, were collated and analyzed over four different time periods (1880–1960, 1961–1980, 1981–1996, and 2019–2021). Moreover, 148 present-day menus from two other cities north (Anchorage, AK, USA) and south (Los Angeles, CA, USA) of Vancouver were also collected. An index, herein called Mean Temperature of Restaurant Seafood (MTRS), was calculated from the average temperature preference of the species of seafood identified in the menus for each time period or location. Overall, the MTRS of menus from Vancouver increased from 10.7 ± 0.7 °C to 13.8 ± 1.0 °C (95% confidence intervals) between 1888–1960 and 2019–2021. Present-day MTRS was among the highest in Los Angeles (16.5 ± 1.7 °C) and lowest in Anchorage (9.6 ± 1.0 °C). The temporal and spatial variations in MTRS are significantly related to observed patterns of average sea surface temperature and the Mean Temperature of the Catch. This suggests that restaurant menus may be used as a complementary information source regarding changes in marine ecosystems and fisheries and the seafood sector’s responses to these changes. This study also highlights the value of using unconventional information sources and their applications in the detection of climate impacts on oceans and their dependent human communities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".