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Record W2518911905

GLOBAL DEMAND FOR U.S. MEATS By

2002· article· en· W2518911905 on OpenAlexaboutno aff
João Mutondo

Bibliographic record

VenueSHAREOK (University of Oklahoma) · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
FundersEconomic Research ServiceOklahoma State UniversityIowa State UniversityU.S. Department of Agriculture
KeywordsEconomicsBusiness
DOInot available

Abstract

fetched live from OpenAlex

Scope and Method of Study: This study is composed of three chapters. The first and second chapters assess the competitiveness of U.S. meats in the major U.S. export markets and in the U.S. domestic market, respectively, taking into account the effects of seasonality and animal disease outbreaks (Bovine Spongiform Encephalopathy-BSE and foot-and-mouth disease-FMD). The restricted source differentiated almost ideal demand system (RSDAIDS) is used to estimate Canadian, Japanese, Mexican, South Korean, and U.S. demand models. The objective of the third chapter is to estimate the welfare impacts of beef and pork promotions, country of origin labeling (COOL), and the Japanese and South Korean bans of U.S. beef on producers and marketers of U.S. meats. An equilibrium displacement model is built and used to estimate welfare measures.

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)0.000
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0110.002

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 head0.024
GPT teacher head0.163
Teacher spread0.140 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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