MétaCan
Menu
Back to cohort
Record W207301244 · doi:10.1086/mre.17.3.42629365

Allocation of Fish Between Markets and Product Forms

2002· article· en· W207301244 on OpenAlexaboutno aff
Frank Asche, Rögnvaldur Hannesson

Bibliographic record

VenueMarine Resource Economics · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsPollockProduct (mathematics)RevenueFish <Actinopterygii>EconomicsFunction (biology)BusinessMicroeconomicsIndustrial organizationFisheryEconometricsMathematics

Abstract

fetched live from OpenAlex

In this paper, we investigate the suppliers' allocation decisions between different product forms and markets using supply equations derived from a translog revenue function. This is of interest based on the hypothesis that fish processors and importers respond to changes in relative prices, diverting more fish into products or to markets where the price has risen. This can also at least partially explain the strong degree of correlation between prices of different product forms and markets that is observed in many seafood markets. An empirical analysis is carried out for cod for three main producers, Canada, Iceland, and Norway. The supply of cod exhibits substantial variation, and it is processed into a number of product forms. How the landings are allocated between product forms is then of substantial interest in itself, but also with respect to the influence of new species in the whitefish market, like pollock.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.001

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.

Opus teacher head0.019
GPT teacher head0.173
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueMarine Resource EconomicsSame topicEconomics of Agriculture and Food MarketsFrench-language works237,207