The Demand and Allocation of Alaskan and Canadian Snow Crab
Bibliographic record
Abstract
For over two decades the Alaskan snow crab industry has been an important component of the economic health of the Alaskan crab fisheries. This has been particularly true given the substantial historical stock declines in the Alaskan king and Tanner crab fisheries. However, dwindling local stocks, combined with substantial increases in snow (Queen) crab from Canada and other countries, has raised considerable concern over the sustained economic health from these fisheries. This paper reports on an econometric model of the Alaskan and Canadian snow crab fishery designed to both document the exvessel and wholesale price and revenue responses to harvest and market conditions, and to document the pre‐crab rationalization market performance of the Alaskan snow crab fishery on the eve of the historic implementation of both harvester and processor quotas. Pendant plus de deux décennies, l'industrie du crabe des neiges a constitué un élément important de la santééconomique de la pêche au crabe en Alaska. C'est particulièrement le cas en raison de la diminution substantielle des stocks historiques de crabe royal et de crabe des neiges du Pacifique. Cependant, la diminution des stocks locaux, combinée à des augmentations substantielles de crabe des neiges provenant du Canada et d'autres pays, a soulevé des inquiétudes considérables quant à la durabilité de la santééconomique de ces pêches. Le présent article présente un modèle économétrique de la pêche au crabe des neiges en Alaska et au Canada conçu pour documenter les prix au débarquement, les prix de gros et les revenus correspondants en fonction des conditions de récolte et de l'état du marché, et pour documenter la performance du marché avant que l'industrie du crabe des neiges en Alaska ne soit rationalisée par l'imposition historique de contingents aux pêcheurs et aux usines de transformation du poisson.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".