Seasonal and Spatial Variations in Demand for and Elasticities of Fish Products in the United States: An Analysis Based on Market‐Level Scanner Data
Bibliographic record
Abstract
Seasonally and spatially varying demand elasticities would provide important information to seafood producers and marketers as well as policy makers. We analyzed the effects of season and space on (i) demand (translation effects) and (ii) price as well as expenditure elasticities of demand (scaling effects) for 13 finfish species in the United States. The paper used market‐level scanner data for 52 U.S. markets. Results suggest that not only the quantity demanded, but also the demand elasticities vary across species, seasons, and geography; not only does the degree of competition among finfish products vary considerably over space, but substituting products themselves change. These results highlight the importance of studying consumer demand behavior at species level, across seasons and geography, particularly as it sheds important light on some important policy issues such as the potential substitution between catfish and tilapia in the U.S. markets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".