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Record W3121391269

Effects Of The Duties On Canadian Hard Red Spring Wheat

2005· article· en· W3121391269 on OpenAlexaboutno aff
Jeremy Mattson, Won W. Koo, Jungho Baek

Bibliographic record

VenueAgribusiness & Applied Economics Report · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsBushelRevenueLiberian dollarAgricultural economicsEconomicsProduction (economics)Agricultural scienceBusinessFinanceEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

Since the United States imposed antidumping and countervailing duties totaling 14.16 percent on imports of Canadian hard red spring (HRS) wheat, Canadian exports to the United States have nearly stopped. This study examines the changes in U.S. wheat imports from Canada. An econometric model is developed and estimated to determine the effects of the decline in HRS wheat imports on U.S. farm price and producer revenue. The substantial decline in HRS wheat imports from Canada from the 1997/98 - 2001/02 levels to the current levels is found to have increased the spring wheat price received by farmers by about $0.15 per bushel. With the average yearly HRS wheat production totaling 481 million bushels, this price increase means an increase in annual income of $74 million. The increase in price also leads to an increase in production, and domestic sales replace imports. This increase in production leads to an additional increase in revenue of $27 million per year. The total increase in revenue for the U.S. HRS wheat industry is about $101 million per year. Some of the decline in Canadian HRS wheat exports to the United States could be due to a weakening U.S. dollar and below average Canadian production, but most is likely due to the U.S. imposition of antidumping and countervailing duties.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.723
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

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

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.

Opus teacher head0.008
GPT teacher head0.166
Teacher spread0.157 · 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 teacher head, 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
Published2005
Admission routes1
Has abstractyes

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