Bibliographic record
Abstract
Abstract Supply–demand models, which are commonly used in food policy analysis, have been recently applied to generate projections of future fish supplies. However, these models routinely ignore stock dynamics; hence, threats to sustainability due to declining fish stocks are addressed, at best, by exogenous changes in resource productivity. Such a device is ad hoc, as it is unclear whether the assumed shifts are consistent with known patterns of population adjustment. On the other hand, bioeconomic models incorporate stock dynamics, but typically omit price adjustment arising from the interaction of demand and supply. An applied supply–demand model with stock dynamics combines the strengths of both approaches. However, data problems constrain the formulation of such a model. Instead, this study presents a prototype bioeconomic supply–demand model. Simulations show trends in fish supply that fail to appear in either supply–demand or bioeconomic models. Secular demand growth causes initial production growth, followed by stagnation, and then persistent decline. Moreover, under constant pressure from rising demand, capture production fails to recover completely from adverse population shock (such as may be induced by climate change). The prototype model highlights the potential usefulness of an applied bioeconomic supply–demand model for food policy and fisheries management, and provides a template for future work.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".